3 May 2012 Robust multiplatform RF emitter localization
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Abstract
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.
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Huthaifa Al Issa, Huthaifa Al Issa, Raúl Ordóñez, Raúl Ordóñez, } "Robust multiplatform RF emitter localization", Proc. SPIE 8402, Evolutionary and Bio-Inspired Computation: Theory and Applications VI, 84020O (3 May 2012); doi: 10.1117/12.918905; https://doi.org/10.1117/12.918905
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