Dr. Huthaifa A. Al Issa Profile

Dr. Huthaifa A. Al Issa

at Univ of Dayton

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Publications (5)

Proceedings Article | 17 October 2014 Paper

PADF electromagnetic source localization using extremum seeking control

Huthaifa Al Issa, Raúl Ordóñez

Proceedings Volume 9248, 92480K (2014) https://doi.org/10.1117/12.2066976

KEYWORDS: Sensors, Sensor networks, Wireless communications, Radar, Control systems, Transmitters, Collision avoidance, IRIS Consortium, Environmental sensing, Source localization

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Proceedings Article | 28 May 2013 Paper

Multi-platform RF emitter localization using extremum seeking control

Huthaifa Al Issa, Raúl Ordóñez

Proceedings Volume 8753, 875302 (2013) https://doi.org/10.1117/12.2014361

KEYWORDS: Sensors, Radar, Sensor networks, Collision avoidance, Error analysis, Control systems, Transmitters, Telecommunications, IRIS Consortium, Computer simulations

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Proceedings Article | 4 May 2012 Paper

Robust multiplatform RF emitter localization

Huthaifa Al Issa, Raúl Ordóñez

Proceedings Volume 8402, 84020O (2012) https://doi.org/10.1117/12.918905

KEYWORDS: Sensors, Transmitters, Sensor networks, IRIS Consortium, Received signal strength, Receivers, Detection and tracking algorithms, MATLAB, Telecommunications, Eye models

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In recent years, position based services has increase. Thus, recent developments in communications and RF technology have enabled system concept formulations and designs for low-cost radar systems using state-of-the-art software radio modules. This research is done to investigate a novel multi-platform RF emitter localization technique denoted as Position-Adaptive RF Direction Finding (PADF). The formulation is based on the investigation of iterative path-loss (i.e., Path Loss Exponent, or PLE) metrics estimates that are measured across multiple platforms in order to autonomously adapt (i.e. self-adjust) of the location of each distributed/cooperative platform. Experiments conducted at the Air-Force Research laboratory (AFRL) indicate that this position-adaptive approach exhibits potential for accurate emitter localization in challenging embedded multipath environments such as in urban environments. The focus of this paper is on the robustness of the distributed approach to RF-based location tracking. In order to localize the transmitter, we use the Received Signal Strength Indicator (RSSI) data to approximate distance from the transmitter to the revolving receivers. We provide an algorithm for on-line estimation of the Path Loss Exponent (PLE) that is used in modeling the distance based on Received Signal Strength (RSS) measurements. The emitter position estimation is calculated based on surrounding sensors RSS values using Least-Square Estimation (LSE). The PADF has been tested on a number of different configurations in the laboratory via the design and implementation of four IRIS wireless sensor nodes as receivers and one hidden sensor as a transmitter during the localization phase. The robustness of detecting the transmitters position is initiated by getting the RSSI data through experiments and then data manipulation in MATLAB will determine the robustness of each node and ultimately that of each configuration. The parameters that are used in the functions are the median values of RSSI and rms values. From the result it is determined which configurations possess high robustness. High values obtained from the robustness function indicate high robustness, while low values indicate lower robustness.

Proceedings Article | 21 June 2011 Paper

PADF RF localization criteria for multimodel scattering environments

Miguel Gates, Christopher Barber, Rastko Selmic, Huthaifa Al-Issa, Raul Ordonez, Atindra Mitra

Proceedings Volume 8021, 80210W (2011) https://doi.org/10.1117/12.879760

KEYWORDS: Sensors, Error analysis, Sensor networks, Scattering, Received signal strength, IRIS Consortium, MATLAB, Robotics, Receivers, Transmitters

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Proceedings Article | 20 May 2011 Paper

PADF RF localization experiments with multi-agent caged-MAV platforms

Christopher Barber, Miguel Gates, Rastko Selmic, Huthaifa Al-Issa, Raul Ordonez, Atindra Mitra

Proceedings Volume 8059, 805903 (2011) https://doi.org/10.1117/12.879849

KEYWORDS: Sensors, Sensor networks, Error analysis, Cameras, Unmanned aerial vehicles, Micro unmanned aerial vehicles, Control systems, IRIS Consortium, Antennas, Data acquisition

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This paper provides a summary of preliminary RF direction finding results generated within an AFOSR funded testbed facility recently developed at Louisiana Tech University. This facility, denoted as the Louisiana Tech University Micro- Aerial Vehicle/Wireless Sensor Network (MAVSeN) Laboratory, has recently acquired a number of state-of-the-art MAV platforms that enable us to analyze, design, and test some of our recent results in the area of multiplatform position-adaptive direction finding (PADF) [1] [2] for localization of RF emitters in challenging embedded multipath environments. Discussions within the segmented sections of this paper include a description of the MAVSeN Laboratory and the preliminary results from the implementation of mobile platforms with the PADF algorithm. This novel approach to multi-platform RF direction finding is based on the investigation of iterative path-loss based (i.e. path loss exponent) metrics estimates that are measured across multiple platforms in order to develop a control law that robotically/intelligently positionally adapt (i.e. self-adjust) the location of each distributed/cooperative platform. The body of this paper provides a summary of our recent results on PADF and includes a discussion on state-of-the-art Sensor Mote Technologies as applied towards the development of sensor-integrated caged-MAV platform for PADF applications. Also, a discussion of recent experimental results that incorporate sample approaches to real-time singleplatform data pruning is included as part of a discussion on potential approaches to refining a basic PADF technique in order to integrate and perform distributed self-sensitivity and self-consistency analysis as part of a PADF technique with distributed robotic/intelligent features. These techniques are extracted in analytical form from a parallel study denoted as "PADF RF Localization Criteria for Multi-Model Scattering Environments". The focus here is on developing and reporting specific approaches to self-sensitivity and self-consistency within this experimental PADF framework via the exploitation of specific single-agent caged-MAV trajectories that are unique to this experiment set.

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