This PDF file contains the front matter associated with SPIE Proceedings Volume 12121, including the Title Page, Copyright information, Table of Contents, and Committee Page.

In contemporary defense training and operations, users regularly encounter complicated and dynamic environments that generate large amounts of knowledge derived from locally acquired data. In order to facilitate collaborative decision making, users need to effectively share and distribute locally learned knowledge in a timely manner. This paper presents a semantic-based knowledge and information sharing system (S-KISS): a forum application for efficient peer-to-peer knowledge sharing. S-KISS enables simple and casual peer-to-peer information exchange, while retaining the quality of widely disseminated content for judicious knowledge consumption. Based on advanced semantic analysis technologies, S-KISS also supports effective semantic-based knowledge searching and semi-automated knowledge management with two knowledge management methods: (1) knowledge similarity searching based on WordNet and BERTScore, and (2) semantic similarity-based knowledge graph construction and knowledge grouping. The searching method focused on the semantics of text instead of word spans. Meanwhile, the grouping method constructs a knowledge graph where each node represents a posting and the links between nodes along with their semantic similarities. Postings can be grouped into multiple clusters of similar topics using Markov clustering algorithm, which allows users to look up related content quickly and effectively. The feasibility and effectiveness of S-KISS is demonstrated via a web-based prototype using practical scenarios and a real-world benchmark dataset curated from the sub-Reddit online forum ‘r/newtothenavy’. With broad and generic language models, the capabilities developed in S-KISS are applicable for knowledge information management in any space, air, sea, marine, and cyber domains. S-KISS can be utilized in other relevant software applications such as collaborative communication platforms and e-training discussion forums.

Modern infrastructure development has led to a rise in deployed surveillance cameras to monitor remote locations and widespread infrastructures. In today’s networked surveillance environment, however, human operators are often overwhelmed with the huge amount of visual feeds, which causes poor judgment and delayed response to emergencies. This paper proposes a distributed crawler scheme (DiCrawler) for smart surveillance systems deployed on Internet of Video Things (IoVT). The IoVT camera nodes monitor continuous video input, track the object of interest while preserving privacy, and relay correlative information to targeted nodes for constant monitoring. Each IoVT node monitors the space inside its field of view (FoV) and notifies the neighboring nodes about the objects leaving the FoV and heading in their directions. A smart communication algorithm among IoVT nodes is designed to prevent network bandwidth bottlenecks and preserve computational power. The DiCrawler system can corroborate with human operators and assist with decision-making by raising alarms in case of suspicious behavior. The IoVT network is completely decentralized, using only peer-to-peer (P2P) communication. DiCrawler does not rely on a central server for any computations, preventing a potential bottleneck if hundreds of cameras were connected and constantly uploading data to a server. Each module is also in a compact form factor, making it viable to be mounted on traditional security surveillance cameras. Extensive experimental study on a proof-of-concept prototype validated the effectiveness of the DiCrawler design.

This paper presents a methodology to study the need and implementation of a GPS-denied navigation system that gives position, velocity and time (PVT) graph. We discuss one such technology that uses an inertial measurement unit (IMU) comprising of accelerometer, gyroscope, magnetometer, and altimeter. We investigate the input and output relationship between a GPS available navigation system (Google Maps) and a GPS-denied navigation system (IMU based AI board). We delineate how to make such a system in a block-wise fashion so that future researchers can get a head start to the area of research that is termed pedestrian dead reckoning (PDR). We show our implementation here which is currently better than the bulk of the research that was found during our time of literature survey. This implementation takes ideas from liquid (NN) machine learning, hybrid convolutional neural network deep learning based online PDR navigation, and generative adversarial network (GAN) based motion transfer learning.

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.

The application of machine learning to a task often necessitates the production of synthetic training data. Some tasks involve rare, but important, scenarios that may not yet have been observed; others are difficult to collect or annotate in large volumes. These difficulties are particularly acute in computer vision applications to scientific imagery, in which human annotation is complicated by noise, ambiguity, and interpretation. One such application is the detection of resident space objects (RSOs) in electro-optical images for space domain awareness (SDA). In many cases, the mislabeling of RSOs by an imperfect annotator (human or machine) can be detrimental to machine learning model performance, especially when the signal-to-noise (SNR) is near or below human detection levels. In this work we introduce SatSim, a modular electro-optical synthetic data generation engine designed to procedurally generate representative, annotated synthetic electro-optical imagery of remote space scenes. SatSim enables rapid generation of synthetic data through Graphics Processing Unit (GPU) acceleration with TensorFlow. This paper discusses the use of SatSim to enhance machine learning approaches and reports the performance of models trained with real data, synthetic data, and real data augmented with synthetic RSOs. In addition, we explore using SatSim to evaluate current state-of-the-art RSO detection algorithms with new sensors (such as all-sky and event-based) and rare but critically important scenarios (such as satellite breakups and collisions) for which limited real data are available.

Extended object imaging of resident space objects is foundational to space domain awareness. Large aperture optical systems can capture spatially resolved imagery of objects in low Earth orbit but are infeasible for objects at higher altitudes. In this work we explore the application of distributed aperture optical systems to extended object imaging at distances beyond low Earth orbit using fully differentiable physical models. Distributed aperture systems are a cost-effective design for large aperture telescopes, but entail a sequential control problem to correct for aberrations induced by phase differences between the spatially distal subapertures. We provide a fully differentiable, joint formulation of this control problem and the associated image recovery task, and train a model to maximize reconstruction quality from an ensemble of focal plane images. We measure the quality of the recovered images, and position these results relative to the recovery quality achieved by monolithic telescopes.

There has been an exponential increase in planned lunar missions with the advent of commercial missions and future manned missions. Understanding the lunar environment is crucial to providing information for future missions that will land on the moon and other celestial bodies. The necessity to navigate freely, without loss of sensing capability is paramount to many of these explorations. Some of the vehicles that will pioneer these missions will encounter various natural hazards and awareness challenges–such as lunar dust and regolith. Due to their electrical and mechanical properties, the constituent particles will tend to adhere to the surface of the exploratory craft and the on-board equipment. This behavior poses a significant threat as the charged dust can cause vital electronics to short or render experimental instruments unusable due to accumulation and electrical discharges, as experienced by the Apollo crews on each visit to the Moon. This paper will focus on the design and experimentation of an autonomous control system that will detect regolith using the deep learning computer vision architecture MobileNetV2, and remove it using an electrodynamic dust shield covering a camera lens. This entire system provides an active method to detect optical obstructions, assess, and then remove them in order to regain navigation and/or scientific capabilities.

There are many space object tracking and identification techniques developed which require an ontology that aligns the operator with space situational awareness (SSA), space weather, and space communications. When considering space object detection, recognition, and classification; preliminary supporting information of the user needs, space environment, and signals bandwidth contribute to space traffic management. The paper discussion seeks to motivate an alignment between space weather and SSA ontologies by presenting notional ideas for developing a space domain awareness (SDA) ontology as a holistic approach that brings together various space community activities and disciplines. Such a SDA ontology is needed to be prepared for the congestion of commercial satellites, proliferation of space debris, and communications link budget analysis.

Space domain awareness (SDA) has become increasingly important as industry and society seek further interest in occupying space for surveillance, communication, and environmental services. To maintain the safe launch and orbit placement of future satellites, there is a need to reliably track the positions and trajectories of discarded launch designs that are debris objects orbiting Earth. In particular, debris objects with sizes on the order of 20 cm or smaller travelling at high speeds maintain enough energy to pierce and permanently damage current functional satellites. The paper presents a theoretical analysis of modeling the radar returns of space debris as simulated signatures for comparison to real measurements. For radar modeling, when the incident radiation wavelength is comparable to the radius of the debris object, Mie scattering is dominant. Mie scattering describes situations where the radiation scatter propagates predominantly, i.e., contains the greatest power density, along the same direction as the incident wave. Mie scatter modeling is especially useful when tracking objects with forward scatter bistatic radar, as the transmitter, target, and receiver lie along the same geometrical trajectory. This paper provides a baseline method towards modeling space debris radar signatures or radar cross-sections (RCS) in relation to the velocity and rotational motions of space debris. The results show the impact of the debris radii varying from 20 cm down to 1 cm as from radiation of comparable wavelength. The resulting scattering nominal mathematical relationships determine how debris size and motion affects the radar signature. It is shown that RCS is proportional to linear size, and that the Doppler shift is predominantly influenced by translation motion.

Mobile edge computing (MEC) is an emerging and fast-growing distributed computing paradigm. It brings the computation and storage resources closer to mobile users while also processing data at the network edge to improve response time and save bandwidth. In tactical virtual training environments, latency is a key factor that affects training performance. Additionally, MEC provides both information service environment and cloud computing capabilities to enable real-time virtual training. Therefore, we designed a machine learning-based data caching and processing scheme for the virtual training networks. The design consists of three tiers, mobile devices, edge servers, and cloud servers, respectively. By pre-caching the critical content objects close to the mobile devices, our MEC network enables data transmission and processing at low latency. Utilizing machine learning techniques, our caching scheme can predict and select the content objects to be cached with optimal storage efficiency at network edge servers. Specifically, we decoupled the content caching problem into two subproblems, namely probability learning and content selection. For probability learning, the edge servers estimate the probability and frequency that each content object will be requested in the near future. The estimate is according to the content request pattern learned over time. For the content selection, the edge servers determine the content objects for caching to minimize the expected content delay with limited storage. To evaluate the performance of our proposed scheme, we developed a testbed with real mobile devices and servers. The experimental results validated the feasibility and significant performance gains of the proposed scheme.

Next-generation (5G & beyond) cellular networks promise much higher throughput and lower latency. However, mobile users experiencing poor channel quality not only suffer low data-rate connections with the base station but also reduce cell’s aggregate throughput and increase overall delay. In this paper, we consider a hybrid cellular and mobile ad hoc Device-to-Device (D2D) network that leverages the advantages of both wide-area cellular coverage and high-speed ad hoc D2D relaying to enhance network performance and scalability. Dedicated relay devices, such as Unmanned Aerial Vehicles (UAVs)/drones, can also be deployed to further improve network connectivity and thus throughput. The base station may send the packets destined for a mobile user with poor cellular channel quality to a proxy mobile device with better cellular channel quality. The proxy mobile device will relay the packets to the destination, thereby significanltly improving network throughput and delay. We formulate the data transmission problem and design an online reinforcement learning-based algorithm to achieve the best transmission performance.

Meteorites and space debris represent a growing threat; their relative speeds make even the tiniest, a potential hazard. Whilst optical and radar instruments allow good coverage of larger debris (> 10cm) population, smaller ones evade most detection attempts. The imaging challenge of probing the population of small size debris is significant as their lower brightness & high speed renders them difficult to see over the zodiacal background. Although the optical tracking of known debris is possible, the detection of uncharted debris implies a staring imager looking for moving objects. An imager capable of short exposures best accomplishes this task, as it prevents the faint object’s signal from being drowned by the background signal. Short exposures further imply that the level of detected signal will be very low; the electron multiplying charge coupled device (EMCCD) technology, with its photon counting capability, coupled to motion-compensation algorithms, can truly boost detection capabilities. This paper will describe the advantages of a ground and space-based EMCCD usage to detect and monitor those high-velocity objects.

A beam expander telescope is a critical key component in satellite based laser communication terminals (LCTs), since it should maintain its optomechanical stability and imaging performance under extreme environmental conditions and launch stresses. In this report we describe the design, development and functional verification of an afocal off-axis catadioptric metal telescope made of AlSi alloy, that is able to satisfy all performance requirements for operation within a LCT. The telescope has an aperture size of 70 mm and a magnification of 5.6x. The diffraction limited performance is accomplished within a tight wave front error (WFE) budget of ≤ 50 nm including defocus. Polarization duplexer functionality is created with a high polarization extinction ratio (PER) of ≥ 20 dB by utilizing a quarter waveplate assembly. The telescope design is optimized to ensure very low stray and false light to the detector while simultaneously providing high transmission at the operating laser wavelength of 1064 nm. The dimensional and thermo-mechanical stability of the telescope is verified by performing thermal-vacuum and vibration tests. All optomechanical structure including light-weighted mirrors are made of a same aluminum alloy (AlSi) with a coefficient of thermal expansion (CTE) matched electroless nickel coated layer. Athermalization of the telescope is obtained by an ocular lens mounted on an aluminum compensator. In addition, we present a calibration unit (CU) design which is separately integrated into the telescope with a beam sampling mirror, allowing the in orbit co-alignment of the transmitter (TX) and receiver (RX) beams. The functional verification of the complete telescope system is successfully demonstrated under thermal-vacuum environment.

Single photon detection (SPD) has found increasingly important applications in many forefront areas of fundamental science and advanced engineering applications. The current SPD scheme has good sensitivity for photons in the high frequencies range (e.g., visible light). However, their sensitivity decreases drastically for low-frequency, low energy, microwave photons. As a result, the detection of single photons at this low frequency is highly prone to error from classical noise. In this talk we will present results from our recent studies of microwave response in a topological superconducting quantum interference device (SQUID) realized in Dirac semimetal Cd3As2. It is observed the effective temperature increases with the microwave power. This observation of large microwave response may pave the way for single photon detection at the microwave frequency in topological quantum materials.

Multispectral infrared remote-sensing is a powerful tool for characterization of space objects. Unfortunately, it is rarely applied due to lack of applicable data. Fortunately, a few relevant historical databases, such as the NASA-wide field infrared explorer (NASA-WISE) space debris database, exist and permit investigation of the properties of space objects. This space debris database includes measurements in the mid-wave and long-wave infrared bands. In this work we demonstrate statistical characterization of unresolved multispectral infrared space debris data, through analysis of statistics of the space object populations observed infrared properties. Space objects are categorized according to derived thermal properties. Statistical tests are developed that allow classification based on new measurements of thermal properties.

The detection of closely spaced artificial satellites informs tactical decision making in a high risk scenario in the space domain. In regimes where spatial information is lost (ground observations of small or distant satellites), spectroastrometry simulations have demonstrated the potential to detect the presence of multiple objects down to 0′′.05–ten meters at geostationary orbit–using a medium resolution optical spectrograph on a large aperture telescope.¹ This technique falls into the growing field of learned space domain awareness: leveraging convolutional neural networks to rapidly infer tactical information from complex, non-intuitive data. In this work we present a field rotation nodding technique that removes the need for a priori knowledge of the closely spaced object on sky orientation. We discuss modifications to an optical spectrograph necessary to perform this technique. We present simulated bounds on the effectiveness of spectroastrometry for the detection of closely spaced objects.

In this paper, the extended Kalman filter is used to estimate the states of an electro-hydrostatic actuator. The filter is then programmed using very high-speed hardware description language (VHDL) and realized using a field programmer gate array (FPGA) prototyping board from Xilinx. The design is then optimized to improve the performance using different techniques. The results show that the implementation of the system requires a minimal amount of FPGA resources.

More sophisticated non-dispersive infrared (NDIR) sensors for gas analysis have been developed in recent years, with many references in the literature. This technique is one of the most cost-effective methods to quantify the concentration of a target gas by measuring its absorption of infrared radiation. Dual channel thermopiles comprised of target and reference filter channels are reliably used to monitor the target gas for NDIR sensors. In recent years, commercial off-the-shelf quad-channel thermopiles with integrated passband infrared absorption filters have become available and enable up to three gas mixture detection and quantification, but there is no truly parallel readout circuit available for signal post processing. These sensors with their high sensitivity, fast response time and no cooling requirement makes them ideal candidates for applications that require monitoring multiple gases in real time. Usually, an NDIR sensor uses a cost-effective micro-controller for signal post processing, this limits the monitoring of multiple gases to a serial readout architecture. In this paper, we present a proof-of-concept non-dispersive infrared-red (NDIR) gas analyzer that has been realized with a quad-channel thermopile and a parallel readout circuitry consisting of a multi-channel digitizer (MCD) application specific integrated circuit (ASIC) and a field programmable gate array (FPGA). The parallel readout architecture will help considerably in the calibration schema. The NDIR gas analyzer will be used in a future space-based instrument application to ensure the safe transfer of sublimated volatiles from a comet sample containment system to a gas containment system within the operational pressure-temperature condition.

Planetary science exploration is transitioning from a focus on remote sensing techniques to in situ instruments for landed missions, and Raman spectrometers are quickly gaining ground as essential to these payloads. To accurately identify targets of interest to planetary science, the Raman spectrometer spectral resolution is required to be better than 0.19 nm. While dispersive spectrometers are a direct way to separate optical radiation into its constituent irradiance spectrum, they have major disadvantage of very inefficient light throughput for high resolution applications because they require very small entrances slits, ~50 μm. This is a major drawback for a stand-off system where target sample illumination size is large and return signals are very weak. Fluorescence is typically brighter than the Raman signal, and in conventional Raman spectroscopy, a slow detector integrates both signals and obscures the Raman signature. To mitigate this, we are developing an ultra-compact, high resolution, high throughput, time-resolved VIS-NIR Raman spatial heterodyne spectrometer (SHS). The SHS replaces the modulation mirror in a high resolution and throughput of a traditional Fourier Transform Spectrometer (FTS) with a stationary grating. The SHS has the same advantage of the FTS, which has two orders of magnitude larger acceptance angle than dispersive spectrometers without sacrificing resolution. In this work we focus on applications to stand-off Raman SHS spectroscopy for the detection of biomarkers and characterization of habitability on planetary surfaces.