Convolutional neural networks (CNNs) are a widely researched neural network architecture that has demonstrated exemplary performance in image processing tasks and applications compared to other popular deep learning and machine learning methods resulting in state-of-the-art performance in many image processing tasks such as image classification and segmentation. CNNs operate on the principle of automated learning of filters or kernels in contrast with hand-crafted digital filters to extrapolate features from images effectively. This paper aims to investigate whether a matrix's determinant can be used to preserve information in CNN convolutional layers. Geometrically the absolute value of the determinant is defined as a scaling factor of the linear transformation resulting from matrix multiplication. When an image's size is reduced into a feature space through a convolutional layer of a CNN, some information is lost. The intuition is that the scaling factor that results from the determinant of the pooling layer matrix can enhance the feature space introducing scaling as a piece of information in the feature space as well as lost relations between adjacent pixels.
Blockchain technology has gained notoriety as the foundation for cryptocurrencies like Bitcoin. However, its possibilities go well beyond that, enabling the deployment of new applications that were not previously feasible as well as enormous improvements to already existing technological applications. Several factors impacting the consensus mechanism must fall within a specific range for a blockchain network to be efficient, sustainable and secure. The long-term sustainability of current networks, like Bitcoin, is in jeopardy due to their relatively uncompromising reconfiguration, which tends to be inflexible, and somewhat independent of environmental circumstances. To provide a systematic methodology for integrating a sustainable and secure adaptive framework, we propose the amalgamation of cognitive dynamic systems theory with blockchain technology, specifically regarding variant network difficulty. A respective architecture was designed with the employment of Long-Short Term Memory (LSTM) to control the difficulty of a network with Proof-of- Work Consensus.
The sliding innovation filter (SIF) is a recently developed estimation technique that has gained widespread use. It is a predictor-corrector filter that utilizes a hyperplane and applies a force to allow estimates to fluctuate about it. SIF belongs to the same family as the smooth variable structure filter and sliding mode observer, and it is stable and robust in the face of uncertainties. This paper discusses the use of SIF for estimating the states of Power Converters, which play a crucial role in Electric Vehicles (EVs) by converting high-voltage DC from the battery to low-voltage AC used by the motor. One of the main challenges in Power Converters is accurately estimating their states, such as input voltage, output voltage, and inductor current, which are critical for optimal control and efficient operation. The SIF has demonstrated promising results in addressing this challenge.
Estimating the position of a unmanned ground vehicle (UGV) that is navigating a complex road is a challenging task. Numerous algorithms have been developed to estimate the maneuvering status of the UGV. In this study, a newly developed filtering technique called the sliding innovation filter (SIF) is combined with multiple model technique to improve the estimation accuracy. The SIF uses the measured states as a discontinuous hyperplane to constrain the estimates to stay close to it. By combining the benefits of both methods, the proposed filter minimizes chatter during position estimation when the UGV is maneuvering. The effectiveness of the proposed method is evaluated on a UGV navigating an S-shaped road, and the results are compared to those obtained using the standard SIF.
KEYWORDS: Machine learning, Education and training, Data modeling, Neurons, Network architectures, Covariance, Performance modeling, Signal filtering, Covariance matrices, Systems modeling
The field of estimation theory is concerned with providing a system with the ability to extract relevant information about the environment, resulting in more effective interaction with the system’s surroundings through more well-informed, robust control actions. However, environments often exhibit high degrees of nonlinearity and other unwanted effects, posing a significant problem to popular techniques like the Kalman filter (KF), which yields an optimal only under specific conditions. One of these conditions is that the system and measurement noises are Gaussian, zero-mean with known covariance, a condition often hard to satisfy in practical applications. This research aims to address this issue by proposing a machine learning-based estimation approach capable of dealing with a wider range of noise types without the need for a known covariance. Harnessing the generative capabilities of machine learning techniques, we will demonstrate that the resultant model will prove to be a robust estimation strategy. Experimental simulations are carried out comparing the proposed approach with other conventional approaches on different varieties of functions corrupted by noises of varying distribution types.
An information filter is one that propagates the inverse of the state error covariance, which is used in the state and parameter estimation process. The term ‘information’ is based on the Cramer-Rao lower bound (CRLB), which states that the mean square error of an estimator cannot be smaller than an amount based on its corresponding likelihood function. The most common information filter (IF) is derived based on the inverse of the Kalman filter (KF) covariance. This paper introduces preliminary work completed on developing the information form of the sliding innovation filter. The SIF is a relatively new type of predictor-corrector estimator based on sliding mode concepts. In this brief paper, the recursive equations used in the sliding innovation information filter (SIIF) are derived and summarized. Preliminary results of application to a target tracking problem are also studied.
KEYWORDS: Field programmable gate arrays, Tunable filters, Signal filtering, Image processing, Signal processing, Control systems, Artificial intelligence, Electronic filtering, Design and modelling, Robotics
Field programmable gate arrays (FPGAs) are increasingly popular due to their customizability, which enables them to be tailored to specific applications, resulting in minimal resource usage that saves energy and space. In this work, we used an FPGA with a Z-board from Xilinx to simulate the application of the sliding innovation filter (SIF) to a robotic arm. SIF is a predictor-corrector filter used for both linear and nonlinear systems to estimate states and/or parameters. It shares similar principles with sliding mode observer and smooth variable structure filter (SVSF) and uses a correction gain derived to satisfy Lyapunov stability, keeping the estimates near the measurements. We tested SIF on a manipulator with two joints (rotational and prismatic), using FPGA to run the simulation while tracking resource utilization. We compared the results with those of SVSF.
The recent generalized unscented transform (GenUT) is formulated into a recursive Kalman filter framework. The GenUT constrains 2n + 1 sigma points and their weights to match the first four statistical moments of a probability distribution. The GenUT integrates well into the unscented Kalman filter framework, creating what we call the generalized unscented Kalman filter (GUKF). The measurement update equations for the skewness and kurtosis are derived within. Performance of the GUKF is compared to the UKF under two studies: noise described by a Gaussian distribution and noise described by a uniform distribution. The GUKF achieves lower errors in state estimation when the UKF uses the heuristic tuning parameter κ = 3 − n. It is also stated that when the parameter κ is tuned to an optimal value, the UKF performs identically to the GUKF. The advantage here is that GUKF requires no such tuning.
KEYWORDS: Signal filtering, Tunable filters, Control systems, Electronic filtering, Nonlinear filtering, Engineering, Optical engineering, Digital filtering, Signal processing
Fault detection and identification strategies utilize knowledge of the systems and measurements to accurately and quickly predict faults. These strategies are important to mitigate full system failures, and are particularly important for the safe and reliable operation of aerospace systems. In this paper, a relatively new estimation method called the sliding innovation filter (SIF) is combined with the interacting multiple model (IMM) method. The corresponding method, referred to as the SIF-IMM, is applied on a magnetorheological actuator which was built for experimentation. These types of actuators are similar to hydraulic-based ones, which are commonly found in aerospace systems. The method is shown to accurately identify faults in the system. The results are compared and discussed with other popular nonlinear estimation strategies including the extended and unscented Kalman filters.
The sliding innovation filter is a newly developed filter that was derived in 2020 to be a predictor-corrector filter. The filter uses the measurement as a hyperplane, and then applies a force that makes the estimates fluctuating around it. The filter works on systems with full ranked measurement matrix (all states are measured). However, once the rank becomes partial, the filter depends highly on the pseudo inverse of the measurement matrix. This means that if the measurement matrix does not have a direct link to the hidden states, then these states will not be correctly estimated. When the system is nonlinear, the problem becomes worse as the Jacobean matrix must be calculated for the measurement matrix before the pseudo inverse is applied. To solve this issue, this paper proposes a new formulation of the SIF that is based on the extended Luenberger filter. The proposed method is tested on extracting the damping ration for a third order system.
KEYWORDS: Signal filtering, Control systems, Nonlinear filtering, Electronic filtering, Aerospace engineering, Systems modeling, Signal processing, Engineering
This study presents the development of a new filter, the sequential sliding innovation filter (SSIF), designed for estimating quantities of interest from noisy measurements. The SIF is formulated in a sequential manner, allowing for multiple updates of estimates, making it well-suited for systems with multiple measured states. The filter is applied to an unmanned ground vehicle (UGV) maneuvering in 2-D path in this study, and the results demonstrate that the SSIF outperforms conventional filter and Kalman Filter (KF) in terms of accuracy and efficiency. The SSIF has the potential for use in signal processing, tracking, and surveillance, making it a valuable tool in various fields.
Earth observation satellites, such as those responsible for monitoring the effects of climate change, require rigorous calibration protocols to account for on-orbit sensor degradation. An increasingly dependable method to address this issue uses the Moon as a reference light source for in-situ calibration. The airborne lunar spectral irradiance (air-LUSI) mission aims to improve the utility of the Moon as an on-orbit calibration target for remote sensing instruments, by tying the currently accepted lunar model to the SI and establishing lunar irradiance on an absolute scale. To this end, air-LUSI collects SI-traceable measurements of lunar irradiance at visible to nearinfrared wavelengths with unprecedented accuracy. A non-imaging telescope is flown at an altitude of 21 km, aboard NASA’s high-altitude ER-2 aircraft, which places the instrument above 95% of the Earth’s atmosphere for clean, minimally obstructed lunar spectra. To fix the optical axis on the Moon during flight, an autonomous control system is required to compensate for aircraft motion and track the Moon across its celestial transit. In this paper, we present an overview of the robotic subsystem used to track the Moon on more than ten high-altitude flights, and the valuable lessons learned from those campaigns. From this insight, a preliminary design for a second-generation robotic telescope mount is presented. Referred to as the HAAMR, it will supplant the current robotics system on future air-LUSI Operational Flight Campaigns, with the nearest field deployment slated for January 2024. We show how this new system is poised to offer a more reliable, accurate, and responsive platform for the air-LUSI instrument to continue collecting data that will ultimately help to improve our understanding of the Earth’s climate.
This paper details the design, fabrication, and development of an improved Nanosatellite Attitude Control Simulator (NACS). The NACS consists of a mock 1U CubeSat (MockSat), tabletop air-bearing, and automatic balancing system (ABS). The MockSat employs a reaction wheel array to exchange momentum with the rigidlyattached air bearing platform, and an inertial measurement unit to obtain orientation and angular velocity estimates. The ABS tunes the Simulator’s center of gravity to coincide with the air bearing’s center of rotation in an effort to minimize gravitational torques. This paper presents the majority of the mechanical design process, as well as future insights into the ABS control system. The NACS will be used to build numerous data sets for the development and training of new machine learning algorithms, as well as to benchmark, test, and compare different estimation and control strategies.
KEYWORDS: Tunable filters, Covariance, Signal filtering, Simulations, Gain switching, Covariance matrices, Systems modeling, Modeling, Electronic filtering, Monte Carlo methods
State estimation strategies play an essential role in the effective operation of dynamic systems by extracting relevant information about the system’s state when faced with limited measurement capability, sensor noise, or uncertain dynamics. The Kalman filter (KF) is one of the most commonly used filters and provides an optimal estimate for linear state estimation problems. However, the KF lacks robustness as it does not perform well in the face of modelling uncertainties and disturbances. The sliding innovation filter (SIF) is a newly proposed filter that uses a switching gain and innovation term, and unlike the KF, it only results in a sub-optimal estimate. However, the SIF has been proven to be robust to modelling uncertainties, disturbances, and ill-conditioned problems. In this work, we propose an adaptive SIF and KF (SIF-KF) estimation algorithm that can detect faulty or uncertain conditions and switch between the KF and SIF gain in the absence or presence of such conditions, respectively. A fault detection mechanism based on the normalized innovation squares (NIS) metric is also presented, which is responsible for triggering the activation of the respective gain in the proposed SIF-KF strategy. Experimental simulations are carried out on a simple harmonic oscillator subject to a fault to demonstrate the proposed SIF-KF’s effectiveness over traditional approaches.
Manufacturing has entered the fourth industrial revolution. Modern manufacturing is reliant on assets such as robotics and computer numerical control (CNC) machine tools. To optimize the performance and value of these assets it would be wise to implement digital twin (DT) technology. DT technology has the ability to provide valuable services to owners of machine tools and other manufacturing assets. The current issue facing DTs is that they currently exist at a lower level of sophistication, meaning they are incapable of implementing more complex services. Cognitive dynamic systems (CDS) are a type of smart system based on human cognition which can augment the performance of many engineering systems. This paper proposes a framework of implementing aspects of CDSs to enable DTs to exist at a higher level of sophistication called the cognitive dynamic digital twin (CDDT). Examples exist in the literature of implementing cognitive based methods to improve DT services, they primarily implement artificial intelligence and estimation based methods. Most of these methods implement only one aspect of cognition at a time. In this work the CDDT framework was implemented to build a DT machine tool wear prediction service. The service was shown to be accurate at predicting the levels of wear in cutting tools. This service utilizing the CDDT framework used each of the aspects of human cognition to augment its performance. This framework can be used by many different sorts of DTs to improve their level of sophistication.
KEYWORDS: Blockchain, Mining, Network security, Telecommunications, Internet of things, Computer security, Machine learning, Information security, Distributed computing, Data processing
As the technological landscape continues rapidly evolving, blockchain technology has been widely integrated and employed in various areas of application. Blockchain, at its core, offers a decentralized method for system security and communication. This is in contrast with classical security systems, which necessitate a central node for data processing and communication, therefore augmenting vulnerability to a single point of failure and attack. Incorporating adaptive subsystems into various blockchain technology features might greatly enhance their functionality without jeopardizing the chain's immutability. Several publications have focused on the analysis of network node data in an effort to offer an adaptive version of the consensus mechanism used in the blockchain process. This paper presents a novel adaptive consensus mechanism that regulates the Proof-of-Work mining difficulty based on the perceived anomalous level of network nodes.
The purpose of this paper is to aid in detecting synthesized video (specifically created through the use of DeepFake) by exploring facial-feature tracking methods. Analyzing individual facial features, should allow for more successful detection of DeepFake videos according to H. Nguyen et al.’s research [22] and A. A. Maksutov’s list of commonly use techniques to identify fabricated media [17]. To detect these facial features in images, Computer Vision techniques such as YOLOv3 [24] can be used. Once detected, object-tracking methods should be explored. This paper will compare the accuracy of three existing object-tracking methods: the minimum-distance approach, the Kalman Filter (KF) method, and the Sliding Innovation Filter (SIF) method. Following this comparison, the paper proposes a novel hybrid object-tracking approach, in which the benefits of the KF method and SIF method are combined to provide a time-gap tolerant object-tracking method. Each of the models are tested on their ability to track multiple objects that follow different trajectories and compared against one another to identify the most effective manner of tracking.
In this work, the newly developed filtering technique referred to as the sliding innovation filter (SIF) is combined with multiple model strategies to enhance the performance of the filter when the system changes its structure and/or parameters. This is particularly useful for a system, such as an aerospace system, experiences a fault and continued operation is critical. The proposed method is tested on an aerospace actuator system and the results are discussed.
As Industry 4.0 evolves with the abundance of data, networking capabilities and new computing technologies, manufacturers are looking for ways to exploit this revolution. The demands of machine tools and their feed drive systems require manufacturers to optimally plan and schedule maintenance actions to minimize costs. These actions can be supplemented by capitalizing on machine data and the idea of cyber-physical systems, with the use of edge and cloud computing, by monitoring important machine characteristics. A substantial benefit to manufacturers would be the ability to monitor the health characteristics of machine tools to aid them in their maintenance planning. Some of the challenges manufacturers face with this are the computing time and effort needed to analyze and evaluate the vast amount of machine data available. A step towards real-time condition monitoring of machine characteristics includes rapid parameter estimation of CNC machine tool systems. The estimation of mass and friction allow for the monitoring of CNC feed drive health. This work proposes the estimation of such parameters from real-world industrial machine tool data. A Feed drive testing procedure is developed for smart data acquisition. Data analysis and recursive least squares methods are used to extract key parameters representative of machine health that are realizable on edge computing devices.
The sliding innovation filter (SIF) is a state and parameter estimation strategy based on sliding mode concepts. It has seen significant development and research activity in recent years. In an effort to improve upon the numerical stability of the SIF, a square-root formulation is derived. The square-root SIF is based on Potter’s algorithm. The proposed formulation is computationally more efficient and reduces the risks of failure due to numerical instability. The new strategy is applied on target tracking scenarios for the purposes of state estimation. The results are compared with the popular Kalman filter.
In this paper, a new state and parameter estimation method is introduced based on the particle filter (PF) and the sliding innovation filter (SIF). The PF is a popular estimation method, which makes use of distributed point masses to form an approximation of the probability distribution function (PDF). The SIF is a relatively new estimation strategy based on sliding mode concepts, formulated in a predictor-corrector format. It has been shown to be very robust to modeling errors and uncertainties. The combined method (PF-SIF) utilizes the estimates and state error covariance of the SIF to formulate the proposal distribution which generates the particles used by the PF. The PF-SIF method is applied on a nonlinear target tracking problem, where the results are compared with other popular estimation methods.
The sliding innovation filter (SIF) is a newly developed filter that may be applied to both linear and non-linear systems. The SIF shares similar principles with sliding mode observers (SMO) and other variable structure filters such as the smooth variable structure filter (SVSF). The SIF utilizes the true trajectory as a hyperplane and forces the estimates to stay within a region of the hyperplane through the use of a discontinuous correction gain. In this paper, the SIF is applied to the well-known complex road estimation problem with nonlinear system function. The results of the application are compared with the SVSF, and future work is discussed.
In this paper, the newly developed sliding innovation filter (SIF) is reformulated to accommodate the ability of extracting the hidden states. This is accomplished by using the well-known Luenberger technique, which is commonly used by observers. In this paper, the SIF is applied to a linear system, which has fewer measurements than states. The results show that the proposed filter extracts the hidden state with small RMSE, as low as 0.1, and small MAE, as low as 1.
Artificial feedforward neural networks (ANN) have been traditionally trained by backpropagation algorithms involving gradient descent algorithms. This is in order to optimize the network’s weights and parameters in the training phase to minimize the out of sample error in the output during testing. However, gradient descent (GD) has been proven to be slow and computationally inefficient in comparison with studies implementing the extended Kalman filter (EKF) and unscented Kalman filter (UKF) as optimizers in ANNs. In this paper, a new method of training ANNs is proposed utilizing the sliding innovation filter (SIF). The SIF by Gadsden et al. has demonstrated to be a more robust predictor-corrector than the Kalman filters, especially in ill-conditioned situations or the presence of modelling uncertainties. In this paper, we propose implementing the SIF as an optimizer for training ANNs. The ANN proposed is trained with the SIF to predict the Mackey-Glass Chaotic series, and results demonstrate that the proposed method results in improved computation time compared to current estimation strategies for training ANNs while achieving results comparable to a UKF-trained neural network.
Amidst the extensive global integration of computer systems and augmented connectivity, there have been numerous difficulties within ensuring confidentiality, integrity and availability across all systems. Malware is an ever-present and persistent challenge for security systems of all sorts. Numerous malware detection methods have been proposed, with traditional approaches no longer providing the necessary protection against evolving attack methodologies and strategies. In recent years, machine learning for malware detection has been investigated with great success. In addition, the analysis of application operation code, or opcode, due to its unavoidable nature, can reveal necessary information about software intention. Visualization of opcode data allows for simple data augmentation and texture analysis. The proposed approach utilizes a simple visual attention module to perform a binary classification task on program data, focusing on visualized application opcode data. The proposed model is tested with an ARM-based Internet of Things (IoT) application opcode dataset. In addition, a comparative analysis, using numerous metrics, is conducted on the proposed model’s performance along with several other algorithms. The results indicate that the proposed method outperformed all other tested techniques in accuracy, recall, precision, and F-score.
With the ever-increasing adoption of interconnected technologies and rapid digitization observed in modern-day life, many online networks and applications face constant threats to the security and integrity of their operations or services. For example, fraudsters and malicious entities are continuously evolving their techniques and approaches to bypass current measures in place to prevent financial fraud, vandalism in online knowledge bases and social networks like Wikipedia, and malicious cyber-attacks. As such, many of the supervised models proposed to detect these malicious actions face degradations in detection performance and are rendered obsolete over time. Furthermore, fraudulent or anomalous data representing these attacks are often scarce or very difficult to access, which further restricts the performance of supervised models. Generative adversarial networks (GANs) are a relatively new class of generative models that rely on unsupervised learning. Moreover, they have proven to effectively replicate the distributions of real data provided to them. These models can generate synthetic data with a degree of quality such that their resemblance to real data is almost indistinguishable, as demonstrated in image and video applications – like with the rise of DeepFakes. Based on the success of GANs in applications involving image-based data, this study examines the performance of several different GAN architectures as an oversampling technique to address the data imbalance issue in credit card fraud data. A comparative analysis is presented in this paper of different types of GANs used to fabricate training data for a classification model, and their impact on the performance of said classifier. Furthermore, we demonstrate that it is possible to achieve greater detection performance using GANs as an oversampling approach in imbalanced data problems.
In modern industrial settings, the quality of maintenance efforts directly influence equipment’s operational uptime and efficiency. Condition monitoring is a common process employed for predicting the health of a technical asset, whereby a predictive maintenance strategy can be adopted to minimize machine downtime and potential losses. Throughout the field, machine learning (ML) methods have become noteworthy for predicting failures before they occur, thereby preventing significant financial costs and providing a safer workplace environment. These benefits from predictive maintenance techniques, are particularly useful in the context of military equipment. Such equipment is often significantly expensive, and untimely machine failure could result in significant human endangerment. In this paper, a prognostic model (PROGNOS) is proposed to predict military equipment’s remaining useful life (RUL) based on their monitoring signals. The main considerations of PROGNOS are expectation maximization tuned Kalman Filter (EM-KF) for signal filtering, a recently introduced feature extraction algorithm (PCA-mRMR-VIF), and predictive LSTM model with an adaptive sliding window. The viability and performance of the proposed model were tested on a highly complex competition dataset: the NASA aircraft gas turbine engine degradation dataset, wherein readings from multiple sensor channels were recorded for degrading machines. According to testing results, we can confidently say that the proposed PROGNOS model was viable and robust overall, proving its general usefulness on all military equipment that emit signals.
The work presented in this paper details the design, development, and functional verification of a nanosatellite attitude control simulator (NACS). The NACS consists of a mock 1U CubeSat (MockSat), tabletop air-bearing, and automatic balancing system (ABS). The MockSat employs a reaction wheel array to exchange momentum with the rigidly attached air bearing platform, and an inertial measurement unit to obtain state estimates. The ABS tunes the center of gravity to coincidence with the center of rotation, in an attempt to minimize gravitational torques. Simulation and experimental results validate the theoretical basis of the PD controller, as well as the implementation of the numerous software and hardware modules. This experimental setup can be used by future researchers to benchmark, test, and compare different estimation and control strategies.
Medical image analysis continues to evolve at an unprecedented rate with the integration of contemporary computer systems. Image registration is fundamental to the task of medical image analysis. Traditional methods of medical image registration are extremely time consuming and at times can be inaccurate. Novel techniques, including the amalgamation of machine learning, have proven to be fast, accurate and reliable. However, supervised learning models are difficult to train due to the lack of ground truth data. Therefore, researchers have endeavoured to explore variant avenues of machine learning, including the implementation of unsupervised learning. In this paper, we continue to explore the use of unsupervised learning for the task of image registration across medical imaging. We postulate that a greater focus on channel-wise data can largely improve model performance. To this end, we employ a sequence generation model, a squeeze excitation network, a convolutional neural network variation of long-short term memory and a spatial transformer network for a channel optimized image registration architecture. To test the proposed approach, we utilize a dataset of 2D brain scans and compare the results against a state-of-the-art baseline model.
KEYWORDS: Clouds, Data storage, Data processing, Computer security, Network architectures, Distributed computing, Network security, Control systems, Computing systems
As data collected through IoT systems worldwide increases and the deployment of IoT architectures is expanded across multiple domains, novel frameworks that focus on application-based criteria and constraints are needed. In recent years, big data processing has been addressed using cloud-based technology, although such implementations are not suitable for latency-sensitive applications. Edge and Fog computing paradigms have been proposed as a viable solution to this problem, expanding the computation and storage to data centers located at the network's edge and providing multiple advantages over sole cloud-based solutions. However, security and data integrity concerns arise in developing IoT architectures in such a framework, and blockchain-based access control and resource allocation are viable solutions in decentralized architectures. This paper proposes an architecture composed of a multilayered data system capable of redundant distributed storage and processing using encrypted data transmission and logging on distributed internal peer-to-peer networks.
Highly distributed connected systems, such as the Internet of Things (IoT), have made their way across numerous fields of application. IoT systems present a method for the connection for various heterogeneous devices across the internet, facilitating the efficient distribution, collection and processing of system-related data. However, while system inter connectivity has aided communication and augmented the effectiveness of integrated technology, it has also increased system vulnerability. To this end, researchers have proposed various security protocols and frameworks for IoT ecosystems. Yet while many suggested approaches augment system security, centralization remains an area of concern within IoT systems. Therefore, we propose the use of a decentralization scheme for IoT ecosystems based on Blockchain technology. The proposed method is inspired by Helium, a public wireless long-range network powered by blockchain. Each network node is characterized by its device properties, which are comprised of local and network-level features. Communication in the network requires the testimony of other companion nodes, ensuring that anomalous behaviour is not accepted and thereby preventing malicious attacks of various sorts.
Malware is a term that refers to any malicious software used to harm or exploit a device, service, or network. The presence of malware in a system can disrupt operations and the availability of information in networks while also jeopardizing the integrity and confidentiality of such information, which poses a grave issue for sensitive and critical operations. Traditional approaches to malware detection often used by antivirus software are not robust in detecting previously unseen malware. As a result, they can often be circumvented by finding and exploiting vulnerabilities of the detection system. This study involves using natural language processing techniques, considering the recent advancements made in the field in recent years, to analyze the strings present in the executable files of malware. Specifically, we propose a topic modeling-based approach whereby the strings of a malware’s executable file are treated as a language abstraction to extract relevant topics, which can then be used to improve a classifier’s detection performance. Finally, through experiments using a publicly available dataset, the proposed approach is demonstrated to be superior in performance to traditional techniques in its detection ability, specifically in terms of performance measures such as precision and accuracy.
Blockchain applications go far beyond cryptocurrency. As an essential blockchain tool, smart contracts are executable programs that establish an agreement between two parties. Millions of dollars of transactions attract hackers at a hastened pace, and cyber-attacks have caused large economic losses in the past. Due to this, the industry is seeking robust and effective methods to detect vulnerabilities in smart contracts to ultimately provide a remedy. The industry has been utilizing static analysis tools to reveal security gaps, which requires an understanding and insight over all possible execution paths to identify known contract vulnerabilities. Yet, the computational complexity increases as the path gets deeper. Recently, researchers have been proposing ML-driven intelligent techniques aiming to improve the efficiency and detection rate. Such solutions can provide quicker and more robust detection options than the traditionally used static analysis tools. As of this publication date, there is currently no published survey paper on smart contract vulnerability detection mechanisms using ML models. In order to set the ground for further development of ML-driven solutions, in this survey paper, we extensively reviewed and summarized a wide variety of ML-driven intelligent detection mechanism from the following databases: Google Scholar, Engineering Village, Springer, Web of Science, Academic Search Premier, and Scholars Portal Journal. In conclusion, we provided our insights on common traits, limitations and advancement of ML-driven solutions proposed for this field.
The airborne lunar spectral irradiance (air-LUSI) mission is an inter-agency partnership between the US National Aeronautics and Space Administration and the US National Institute of Standards and Technology. Air-LUSI aims to make SI-traceable measurements of lunar spectral irradiance at visible to near-infrared wavelengths with unprecedented accuracy. To minimize uncertainty, lunar spectra are acquired above 90 % of the Earth’s atmosphere aboard NASA’s Earth Resources aircraft, a civilian descendant of the U-2 spy plane. The data collected by the air-LUSI instrument is poised to improve upon current lunar calibrations of Earth observing satellites. The air-LUSI team recently completed their Operational Flight Campaign in Palmdale, California in March 2022. In addition to the Engineering Flight Campaign of August 2018 and the Demonstration Flight Campaign of November 2019, the air-LUSI instrument has been successfully deployed on over ten lunar spectral measurement flights at altitudes of roughly 21 km. This paper presents the simplified double gimbal design that was capable of recently tracking the Moon with a root mean square tracking error of less than 0.1°.
Kalman filtering (KF) is a widely used filtering technique in highly predictable temporal-mechanical systems where system noise can be modelled with a gaussian function. Improving the signal quality during acquisition is conventionally accomplished by increasing integration time in acquisition. However, this increases the signal acquisition time in photonic systems. In high noise applications, acquisition time is low, and this post-process filtering technique can be applied to increase signal quality. This work explores the comparison of the KF, and nonlinear filtering methods to a simulated blackbody radiation signal where gaussian noise is added to mimic electrical interference. Three filters are selected for comparison on the ability to improve the root mean square error (RMSE) of a simulated measured signal with respect to a simulated actual signal. The filters that are compared in this work are the Extended Kalman Filter (EKF), the Unscented Kalman (UKF), and the Extended Sliding Innovation Filter (ESIF). The filters use a calibration temperature that the filter model uses to determine expected values. To compare the filters, the RMSE is evaluated when error is introduced to the simulation by changing the actual temperature to values equal, below, and above the calibration temperature. Two additional scenarios were considered to test filter robustness. The first scenario uses changes in model temperature occurring as a function of wavelength (i.e., temperature change mid-scan). The second scenario introduces impurities with different emission values. The ESIF demonstrated favorable performance over the other considered filters, showing promise in optical applications.
The sliding innovation filter is a new type of predictor-corrector estimation method. The strategy is used to estimate relevant states of interests and has been found to be robust to modeling uncertainties and disturbances. In this paper, a second-order formulation of the sliding innovation filter is presented to improve its estimation performance in terms of accuracy while maintaining robustness. The strategy is applied to an aerospace system that has been designed for experimentation. The results are compared with the well-known Kalman filter, and future work is considered.
This paper contains a comparison of several sigma-point Kalman filters, including the unscented Kalman filter (UKF), the cubature Kalman filter (CKF), and the central difference Kalman filter (CDKF). The comparison is based on a simulated electro-hydrostatic actuator, which is commonly used for flight surface actuation in aerospace systems. This brief study compares the response, convergence rate, root mean square error, the maximum absolute error, and the stability of these sigma-point Kalman filters.
The applications of unmanned aerial systems (UASs) have grown in popularity due to their simplicity and availability. The quality of UAS’s performance depends usually on adding several sensors and controllers that improve accuracy and flight performance. However, this typically increases the overall cost of the system. In this paper, a technique to enhance the performance while maintaining UAS affordability is proposed. This technique involves the use of an estimation strategy to extract hidden information from only a few sensors while improving the quality of the achieved signal. The simulation results of this method show strong performance, and are compared with another well-known estimation method.
This brief work introduces the use of the relatively new sliding innovation filter in the field of fault detection and diagnosis. This important area is part of signal processing techniques that are widely used in industrial practice, telecommunications, optical systems, and robotics, to name a few. This filter overcomes robustness issues during faults caused by modeling uncertainties. This brief work explores the properties and quality of the filter outputs applied on an electromechanical system. The results are compared with the well-known and studied Kalman Filter.
The sliding innovation filter (SIF) is a newly developed filter that shares similar principles with sliding mode observers and variable structure techniques. The SIF is formulated as a predictor-corrector method that uses the innovation or measurement error as a switching hyperplane and forces the states to remain within a region of its state trajectory. In this brief paper, the SIF is reformulated as a two-pass smoother to reduce the effects of noise and improve the overall performance. The proposed method, known as the sliding innovation smoother (SIS), is applied on an aerospace flight surface actuator, and the results are compared to the original filter.
In this brief work, a novel filtering technique that combines the newly developed sliding innovation filter with a multiple model strategy is proposed. Introduced in 2020, the sliding innovation filter is a relatively new filter used for state and parameter estimation. Based on variable structure techniques, it shares the same principles with sliding mode observers. The filter is robust and stable under system modeling uncertainties. The proposed method multiple model-based sliding innovation filter is tested on an electrohydrostatic actuator (EHA) and the results are discussed.
air-LUSI is a NASA sponsored project which uses optical and robotic equipment to autonomously capture radiometric measurements of the Moon from within the science pod of an ER-2 aircraft while flying at an altitude of 70,000 feet. The air-LUSI instrument was deployed for its first engineering flight campaign on August 1st and 2nd, 2018 and captured the worlds first High Altitude Lunar Spectral Irradiance (LUSI) measurements from a semi-ground based system. By implementing instrumentation into NASA's ER-2 aircraft to produce an Airborne Lunar Observatory, unprecedented LUSI measurements can be obtained that are unadulterated from the Earth's atmosphere. By compiling a comprehensive LUSI dataset for a series of lunar phases, a Lunar Calibration Model can be further refined to provide enhanced remote sensing capabilities for some instruments in NASA's Earth Observing System (EOS). This document presents information about the flight path of the ER-2 to capture High Altitude LUSI measurements, the mechanical design of the robotic telescope, the environmental operating conditions of the design, the in flight tracking performance of the system, and the first raw lunar spectrum captured at 70,000 feet.
The modern farm is a technological marvel, from smart tractors to genetically modified organisms (GMOs), along with chemical pesticides and fertilizer. Farms today have continuously increased production by utilizing these various techniques. Many farms on the east coast of North America are growing dent or field corn while also rotating crops between soybeans of various types and winter wheat. These crops have become symbiotic in nature due to the need for specific soil nutrients of the crops and the practice of no till farming. More recently, schools with farm programs have started researching the use of drone technologies and multispectral analysis as a means to reduce chemical usage thereby saving farmers annual chemical costs. This paper investigates the use of drones in capstone projects for undergraduate engineering and computer science programs. Undergraduate capstone projects usually require a design and build element to satisfy ABET accreditation requirements. Therefore, the students needed to design and build an airframe capable of surveying farms with a multispectral camera. In the course of the aircraft design process it was discovered that the students needed to have a broader understanding of federal regulations, experimentation, and a robust understanding of how the drones and data would be used to benefit a typical farm. In addition, we look at the results obtained and discuss the problems associated with making the data and analysis accessible to the farmers who participated in our study. In the process we also discovered other potential uses for the images we created.
In this paper, an experimental study is performed to find the relation between the current of a battery and the power thrust of an electric-powered ducted fan. Electric-powered duct fans are becoming increasingly popular in unmanned aerial vehicles (UAVs) and are controlled by a pulse position modulation controller. Three different measurements are taken by three transducers, namely: a multimeter with a range of 0 to 400 DC Amps that measures the input current feeding the electric speed controller from the batteries; a load cell with a range of 0 to 45 KG to measure the thrust output of each of the motor; and, a thermocouple to measure the temperature of the Li-Po batteries. Once the data was obtained, an artificial neural network was trained and tested to obtain the relationship between the input (pulse position modulation) and output (the thrust). The effects of battery current on an electric-powered ducted fan are then summarized.
The modern farm is a technological marvel, from smart tractors to genetically modified organisms (GMOs), along with chemical pesticides and fertilizer. Farms today have continuously increased production by utilizing these various techniques. Many farms on the east coast of North America are growing dent or field corn while also rotating crops between soybeans of various types and winter wheat. These crops have become symbiotic in nature due to the need for specific soil nutrients of the crops and the practice of no till farming. More recently, schools with farm programs have started researching the use of drone technologies and multispectral analysis as a means to reduce chemical usage thereby saving farmers annual chemical costs. This paper investigates the use of drones in capstone projects for undergraduate engineering and computer science programs. Undergraduate capstone projects usually require a design and build element to satisfy ABET accreditation requirements. Therefore, the students needed to design and build an airframe capable of surveying farms with a multispectral camera. In the course of the aircraft design process it was discovered that the students needed to have a broader understanding of federal regulations, experimentation, and a robust understanding of how the drones and data would be used to benefit a typical farm. In addition, we look at the results obtained and discuss the problems associated with making the data and analysis accessible to the farmers who participated in our study. In the process we also discovered other potential uses for the images we created.
KEYWORDS: Filtering (signal processing), Systems modeling, Switches, Switching, Error analysis, Space based lasers, Electronic filtering, Monte Carlo methods, Smoothing, Nonlinear filtering
State estimation strategies play a critical role in obtaining accurate information about the state of dynamic systems as they develop. Such information can be important on its own and critical for precise and predictable control of such systems. The Kalman filter (KF) is a classic algorithm and among the most powerful tools in state estimation. The Kalman filter however can be sensitive to modeling uncertainty and sudden changes in system dynamics. The Smooth Variable Structure Filter (SVSF) is a relatively new estimation strategy that operates on variable structure concepts. In general, the SVSF has the advantage that is can be quite robust to modeling uncertainty and sudden fault conditions. Recent advancements to the SVSF, such as the addition of a covariance formulation, and the derivation of a time varying smoothing boundary layer (VBL), have allowed for combined SVSF – KF strategies. In a typical SVSF-KF approach, the VBL is used to detect the presence of a system fault, and switch from the more optimal KF gain to the more robust SVSF gain. While this approach has been proven effective in several cases, there are circumstances where the VBL will fail to indicate the presence of an ongoing fault. A new form of the SVSF-KF is proposed, based on the framework of the Multiple Model Adaptive Estimator.
Estimation theory is an important field in mechanical and electrical engineering, and is comprised of strategies that are used to predict, estimate, or smooth out important system state and parameters. The most popular and well-studied estimation strategy was developed over 60 years ago, and is referred to as the Kalman filter (KF). The KF yields the optimal solution in terms of estimation error for linear, well-known systems. Other variants of the KF have been developed to handle modeling uncertainties, non-Gaussian noise, and nonlinear systems and measurements. Although KF-based methods typically work well, they lack robustness to uncertainties and external disturbances – which are prevalent in signal processing and target tracking problems. The smooth variable structure filter (SVSF) was introduced in an effort to provide a more robust estimation strategy. In an effort to improve the robustness and filtering strategy further, this paper introduces an adaptive form of the SVSF based on the static multiple model strategy.
Hyperspectral image technology is a powerful tool, but oftentimes the data dimension of hyperspectral images must be reduced for practical purposes, depending on the target and environment. For detecting defects on a variety of apple cultivars, this study used hyperspectral data spanning the visible (400 nm) to near-infrared (1000 nm). This paper presents the preliminary results from the selection of optimal spectral bands within that region, using a sequential feature selection method. The selected bands are used for multispectral detection of apple defects by a classification model developed using support vector machine (SVM). As a result, five optimal wavelengths were selected as key features. When using optimal wavelengths, the accuracy of the SVM and SVM with RBF kernel achieved accuracies over 90% for both the calibration and validation data set. However, the results of SVM with RBF kernel (>80%) based on image was more robust than SVM model (>50%). Moreover, SVM with RBF model classified between bruise and sound regions as well specular. The result from this study showed the feasibility of developing a rapid multispectral imaging system based on key wavelengths.
To keep pace with population growth, farmers are leveraging a host of new technologies to improve crop production, including genetically modified organisms (GMOs), along with increased chemical pesticides and fertilizer usage. These new techniques, however, have sometimes led to runoff problems for water systems and local watersheds. By using dronebased technologies the overuse of fertilizers, chemical sprays, and pesticides can be minimized, while preserving farm output and quality. This paper discusses lessons learned from and progress made in a year-long capstone research and development project performed by engineering and computer science students at York College of Pennsylvania. The project involves the study and use of multispectral camera technologies along with drones to survey farms growing corn in various climates. The technologies used to assess farms and modern farming practices are by their nature multidisciplinary. Students involved with this project have thus needed to draw on their engineering and scientific backgrounds while learning new and varied topics to tackle this real-world problem. This paper also examines some of the teaching challenges encountered when using project-based learning (PBL) techniques with engineering students to tackle a multidisciplinary problem similar to the types they will likely face in their professional careers. For example, the students have needed to apply best principles to design and build a drone system to assess crop health. Moreover, they have needed to understand the legal responsibilities of operating drones, farmer issues, and a host of technologies unfamiliar to them prior to this project. Student metrics and outcomes are also assessed to improve the process for future years.
Applications of machine vision techniques are prevalent for quality inspection of foods. For safety inspection of fruits such as apples to detect biological contaminants, a method to capture and reconstruct a whole-surface of apple is needed. In this paper, we present a reconstruction method for whole-surface imaging of apples with the use of a line-scan hyperspectral imaging technique. In addition, the development of online whole-surface inspection technology for round-fruits is presented.
Signal processing techniques are prevalent in a wide range of fields: control, target tracking,
telecommunications, robotics, fault detection and diagnosis, and even stock market analysis, to name a few.
Although first introduced in the 1950s, the most popular method used for signal processing and state
estimation remains the Kalman filter (KF). The KF offers an optimal solution to the estimation problem
under strict assumptions. Since this time, a number of other estimation strategies and filters were introduced
to overcome robustness issues, such as the smooth variable structure filter (SVSF). In this paper, properties
of the SVSF are explored in an effort to detect and diagnosis faults in an electromechanical system. The
results are compared with the KF method, and future work is discussed.
The smooth variable structure filter (SVSF) has seen significant development and research activity in recent years. It is based on sliding mode concepts, which utilize a switching gain that brings an inherent amount of stability to the estimation process. In an effort to improve upon the numerical stability of the SVSF, a square-root formulation is derived. The square-root SVSF is based on Potter’s algorithm. The proposed formulation is computationally more efficient and reduces the risks of failure due to numerical instability. The new strategy is applied on target tracking scenarios for the purposes of state estimation, and the results are compared with the popular Kalman filter. In addition, the SVSF is reformulated to present a two-pass smoother based on the SVSF gain. The proposed method is applied on an aerospace flight surface actuator, and the results are compared with the Kalman-based two-pass smoother.
The goal of this project was to construct a cart and a mounting system that would allow a hyperspectral laser-induced fluorescence imaging system (HLIFIS) to be used to detect fecal material in produce fields. Fecal contaminated produce is a recognized food safety risk. Previous research demonstrated the HLIFIS could detect fecal contamination in a laboratory setting. A cart was designed and built, and then tested to demonstrate that the cart was capable of moving at constant speeds or at precise intervals. A mounting system was designed and built to facilitate the critical alignment of the camera’s imaging and the laser’s illumination fields, and to allow the HLIFIS to be used in both field and laboratory settings without changing alignments. A hardened mount for the Powell lens that is used to produce the appropriate illumination profile was also designed, built, and tested.
KEYWORDS: Artificial neural networks, Neural networks, Control systems, Data storage, Aluminum, Evolutionary algorithms, Transducers, Data acquisition, Systems modeling, Nondestructive evaluation
Structural health prognostics and diagnosis strategies can be classified as either model or signal-based. Artificial neural network strategies are popular signal-based techniques. This paper proposes the use of helicopter blades in order to study the sensitivity of an artificial neural network to structural fatigue. The experimental setup consists of a scale aluminum helicopter blade exposed to transverse vibratory excitation at the hub using single axis electrodynamic shaker. The intent of this study is to optimize an algorithm for processing high-dimensional data while retaining important information content in an effort to select input features and weights, as well as health parameters, for training a neural network. Data from accelerometers and piezoelectric transducers is collected from a known system designated as healthy. Structural damage will be introduced to different blades, which they will be designated as unhealthy. A variety of different tests will be performed to track the evolution and severity of the damage. A number of damage detection and diagnosis strategies will be implemented. A preliminary experiment was performed on aluminum cantilever beams providing a simpler model for implementation and proof of concept. Future work will look at utilizing the detection information as part of a hierarchical control system in order to mitigate structural damage and fatigue. The proposed approach may eliminate massive data storage on board of an aircraft through retaining relevant information only. The control system can then employ the relevant information to intelligently reconfigure adaptive maneuvers to avoid harmful regimes, thus, extending the life of the aircraft.
To achieve comprehensive online quality and safety inspection of fruits, whole-surface sample presentation and imaging regimes must be considered. Specifically, sample presentation method for round objects is under development to achieve effective whole-surface sample evaluation based on the use of a single hyperspectral line-scan imaging device. In this paper, a whole-surface round-object imaging method using hyperspectral line-scan imaging techniques is presented.
In this paper, a comprehensive comparison is made of the following sigma-point Kalman filters: unscented Kalman filter (UKF), cubature Kalman filter (CKF), and the central difference Kalman filter (CDKF). A simulation based on a complex maneuvering road (an s-path) is used as a benchmark problem. This paper studies the response, stability, robustness, convergence, and computational complexity of the filters. Future work will look at implementing the methods on a robot built for experimentation.
This paper is based on a proposed unmanned aerial system platform that is to be outfitted with high-resolution sensors. The proposed system is to be tethered to a moveable ground station, which may be a research vessel or some form of ground vehicle (e.g., car, truck, or rover). The sensors include, at a minimum: camera, infrared sensor, thermal, normalized difference vegetation index (NDVI) camera, global positioning system (GPS), and a light-based radar (LIDAR). The purpose of this paper is to provide an overview of existing methods for pollution detection of failing septic systems, and to introduce the proposed system. Future work will look at the high-resolution data from the sensors and integrating the data through a process called information fusion. Typically, this process is done using the popular and well-published Kalman filter (or its nonlinear formulations, such as the extended Kalman filter). However, future work will look at using a new type of strategy based on variable structure estimation for the information fusion portion of the data processing. It is hypothesized that fusing data from the thermal and NDVI sensors will be more accurate and reliable for a multitude of applications, including the detection of pollution entering the Chesapeake Bay area.
We propose a framework for intelligence, reconnaissance, and surveillance using an aerial vehicle with multiple sensor payloads to provide autonomous and continuous security operations at a fixed location. A control scheme and a graphical user interface between the vehicle and operator is strictly mandated for tasks requiring remote and unattended inspection. By leveraging existing navigation and path planning algorithms, the system can autonomously patrol large areas, automatically recharge when required, and relay on-demand data back to the user. This paper presents recent validation results of the system and its sensors using the proposed framework.
Unmanned ground vehicles have been utilized in the last few decades in an effort to increase the efficiency of agriculture, in particular, by reducing labor needs. Unmanned vehicles have been used for a variety of purposes including: soil sampling, irrigation management, precision spraying, mechanical weeding, and crop harvesting. In this paper, unmanned ground vehicles, implemented by researchers or commercial operations, are characterized through a comparison to other vehicles used in agriculture, namely airplanes and UAVs. An overview of different trade-offs of configurations, control schemes, and data collection technologies is provided. Emphasis is given to the use of unmanned ground vehicles in food crops, and includes a discussion of environmental impacts and economics. Factors considered regarding the future trends and potential issues of unmanned ground vehicles include development, management and performance. Also included is a strategy to demonstrate to farmers the safety and profitability of implementing the technology.
Electric motors are becoming increasingly popular for the propulsion and control of unmanned systems. In order to optimize power generation and energy use for unmanned systems, it is important to understand the dynamics of electric motors and the corresponding powertrain. This paper provides an early, preliminary study on an electric motor used for unmanned aerial systems (UAS’). An electric motor dynamometer is used for collecting data on the motor, and trends are discussed. Future work will look at implementing mathematical models in an unmanned ground system built for experimentation.
Many mechanical and electrical systems have utilized the proportional-integral-derivative (PID) control strategy. The concept of PID control is a classical approach but it is easy to implement and yields a very good tracking performance. Unmanned aerial vehicles (UAVs) are currently experiencing a significant growth in popularity. Due to the advantages of PID controllers, UAVs are implementing PID controllers for improved stability and performance. An important consideration for the system is the selection of PID gain values in order to achieve a safe flight and successful mission. There are a number of different algorithms that can be used for real-time tuning of gains. This paper presents two algorithms for gain tuning, and are based on the method of steepest descent and Newton’s minimization of an objective function. This paper compares the results of applying these two gain tuning algorithms in conjunction with a PD controller on a quadrotor system.
KEYWORDS: Algorithm development, Systems modeling, Wind energy, Data modeling, Control systems, Surveillance, Mathematical modeling, Robotics, Statistical modeling, Data acquisition
Energy storage is one of the most important determinants of how long and far a small electric powered unmanned aerial system (UAS) can fly. For years, most hobby and experimentalists used heavy fuels to power small drone-like systems. Electric motors and battery storage prior to the turn of the century were either too heavy or too inefficient for flight times of any usable duration. However, with the availability of brushless electric motors and lithium-based batteries everything has changed. Systems like the Dragon Eye, Pointer, and Raven are in service performing reconnaissance, intelligence, surveillance, and target acquisition (RISTA) for more than an hour at a time. More recently, multi-rotor vehicles have expanded small UAS capabilities to include activities with hovering and persistent surveillance. Moreover, these systems coupled with the surge of small, low-cost electronics can perform autonomous and semi-autonomous missions not possible just ten years ago. This paper addresses flight time limitation issues by proposing an experimental method with procedures for system identification that may lead to modeling of energy storage in electric UAS’. Consequently, this will allow for energy storage to be used more effectively in planning autonomous missions. To achieve this, a set of baseline experiments were designed to measure the energy consumption of a mid-size UAS multi-rotor. Several different flight maneuvers were considered to include different lateral velocities, climbing, and hovering. Therefore, the goal of this paper is to create baseline flight data for each maneuver to be characterized with a certain rate of energy usage. Experimental results demonstrate the feasibility and robustness of the proposed approach. Future work will include the development of mission planning algorithms that provide realistic estimates of possible mission flight times and distances given specific mission parameters.
This paper addresses the use of unmanned aerial systems (UAS) to carry out atmospheric data collection and studies. An important area of research is the study of the chemistry and physics of Earth's planetary boundary layer (PBL). The PBL, also known as the atmospheric boundary layer (ABL), is the lowest part of the atmosphere and its behavior is directly influenced by its contact with the planetary surface. Sampling of the PBL is performed in a timely and periodic manner. Currently, sensors and uncontrollable balloons are used to obtain relevant data and information. This method is cumbersome and can be ineffective in obtaining consistent environmental data. This paper proposes the use of autonomous UAS’ to study the atmosphere in an effort to improve the efficiency and accuracy of the sampling process. The UAS setup and design is provided, and preliminary data collection information is shared.
Unmanned aerial systems (UAS) are becoming increasingly visible in our daily lives; and range in operation from search and rescue, monitoring hazardous environments, and to the delivery of goods. One of the most popular UAS are based on a quad‐rotor design. These are typically small devices that rely on four propellers for lift and movement. Quad‐rotors are inherently unstable, and rely on advanced control methodologies to keep them operating safely and behaving in a predictable and desirable manner. The control of these devices can be enhanced and improved by making use of an accurate dynamic model. In this paper, we examine a simple quadrotor model, and note some of the additional dynamic considerations that were left out. We then compare simulation results of the simple model with that of another comprehensive model.
The smooth variable structure filter (SVSF) is a state and parameter estimation strategy based on sliding mode concepts. It has seen significant development and research activity in recent years. In an effort to improve upon the numerical stability of the SVSF, a square-root formulation is derived. The square-root SVSF is based on Potter’s algorithm. The proposed formulation is computationally more efficient and reduces the risks of failure due to numerical instability. The new strategy is applied on target tracking scenarios for the purposes of state estimation. The results are compared with the popular Kalman filter.
The smooth variable structure filter (SVSF) has seen significant development and research activity in recent years. It is based on sliding mode concepts, which utilizes a switching gain that brings an inherent amount of stability to the estimation process. In this paper, the SVSF is reformulated to present a two-pass smoother based on the SVSF gain. The proposed method is applied on an aerospace flight surface actuator, and the results are compared with the popular Kalman-based two-pass smoother.
Unmanned aerial systems (UAS) are becoming increasingly popular in industry, military, and social environments. An UAS that provides good operating performance and robustness to disturbances is often quite expensive and prohibitive to the general public. To improve UAS performance without affecting the overall cost, an estimation strategy can be implemented on the internal controller. The use of an estimation strategy or filter reduces the number of required sensors and power requirement, and improves the controller performance. UAS devices are highly nonlinear, and implementation of filters can be quite challenging. This paper presents the implementation of the relatively new cubature smooth variable structure filter (CSVSF) on a quadrotor controller. The results are compared with other state and parameter estimation strategies.
The most popular and well-studied estimation method is the Kalman filter (KF), which was introduced in the
1960s. It yields a statistically optimal solution for linear estimation problems. The smooth variable structure
filter (SVSF) is a relatively new estimation strategy based on sliding mode theory, and has been shown to be
robust to modeling uncertainties. The SVSF makes use of an existence subspace and of a smoothing boundary
layer to keep the estimates bounded within a region of the true state trajectory. This article discusses the
application of two estimation strategies (the KF and the SVSF) on a multi-target tracking problem.
In this paper, we study a nonlinear bearing-only target tracking problem using four different
estimation strategies and compare their performances. This study is based on a classical ground
surveillance problem, where a moving airborne platform with a sensor is used to track a moving
target. The tracking scenario is set in two dimensions, with the measurement providing angle
observations. Four nonlinear estimation strategies are used to track the target: the popular
extended and unscented Kalman filters (EKF/UKF), the particle filter (PF), and the relatively new
smooth variable structure filter (SVSF). The SVSF is a predictor-corrector method used for state
and parameter estimation. It is a sliding mode estimator, where gain switching is used to ensure
that the estimates converge to true state values. An internal model of the system, either linear or
nonlinear, is used to predict an a priori state estimate. A corrective term is then applied to
calculate the a posteriori state estimate, and the estimation process is repeated iteratively. The
performances of these methods applied on a bearing-only target tracking problem are compared
in terms of estimation accuracy and filter robustness.
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