23 May 2013 Sensor selection for target localization in a network of proximity sensors and bearing sensors
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Abstract
The work considers sensor fusion in a heterogeneous network of proximity and bearings-only sensors for multiple target tracking. Specifically, various particle implementations of the probability hypothesis density filter are proposed that consider two different fusion strategies: 1) the traditional iterated-corrector approach, and 2) explicit fusion of the multitarget density. This work also investigates sensor type (proximity or bearings-only) selection via the Renyi entropy criteria. The simulation results demonstrate comparable localization performances for the two fusion methods, and they show that sensor type selection usually outperforms single sensor type performance.
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Qiang Le, Lance M. Kaplan, "Sensor selection for target localization in a network of proximity sensors and bearing sensors", Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 874509 (23 May 2013); doi: 10.1117/12.2017907; https://doi.org/10.1117/12.2017907
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