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Proceedings Paper

An approximate CPHD filter for superpositional sensors
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Paper Abstract

Most multitarget tracking algorithms, such as JPDA, MHT, and the PHD and CPHD filters, presume the following measurement model: (a) targets are point targets, (b) every target generates at most a single measurement, and (c) any measurement is generated by at most a single target. However, the most familiar sensors, such as surveillance and imaging radars, violate assumption (c). This is because they are actually superpositional-that is, any measurement is a sum of signals generated by all of the targets in the scene. At this conference in 2009, the first author derived exact formulas for PHD and CPHD filters that presume general superpositional measurement models. Unfortunately, these formulas are computationally intractable. In this paper, we modify and generalize a Gaussian approximation technique due to Thouin, Nannuru, and Coates to derive a computationally tractable superpositional-CPHD filter. Implementation requires sequential Monte Carlo (particle filter) techniques.

Paper Details

Date Published: 18 May 2012
PDF: 11 pages
Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 83920K (18 May 2012); doi: 10.1117/12.975965
Show Author Affiliations
Ronald Mahler, Lockheed Martin MS2 (United States)
Adel El-Fallah, Scientific Systems Co., Inc. (United States)


Published in SPIE Proceedings Vol. 8392:
Signal Processing, Sensor Fusion, and Target Recognition XXI
Ivan Kadar, Editor(s)

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