Mr. Christopher R. Barber Profile

Christopher R. Barber

at Louisiana Tech Univ

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Author

Publications (2)

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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