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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.
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