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2 May 2017 Dynamic data association for multi-sensor using self-organizing FNN in clutter
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In this paper, improving data association process by increasing the probability of detecting valid data points (measurements obtained from ESM/RADAR system) in the presence of noise for location and target tracking are discussed. This develop a multisensor data association algorithm that fuses information from the multiple ESM receiver and surveillance RADAR. The develop a novel algorithm by self-organizing fuzzy neural network (SO-FNN) for multiple ESM-to-ESM (measurement-to-measurement data association) and ESMs-to-RADAR (track-to-track data association) problem in dense clutter environment. An adaptive search based on SO-FNN of the distance threshold measure is then used to detect valid filtered data point for data association. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithm. The paper is organized as follows. Section 1 is the problem formulation. Section 2 design the new data association algorithm based on SO-FNN data association system design. Section 3 describes ESM-to-ESM and ESM-to-RADAR (measurement-to-measurement data association and track-to-track data association) scenario and the simulation results are presented and discussed. The summary are drawn in section 4, respectively.
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Chi-Shun Hsueh "Dynamic data association for multi-sensor using self-organizing FNN in clutter", Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 102001K (2 May 2017);

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