An approximate CPHD filter for superpositional sensors

Ronald Mahler; Adel El-Fallah

doi:10.1117/12.975965

17 May 2012 An approximate CPHD filter for superpositional sensors

Ronald Mahler, Adel El-Fallah

Proceedings Volume 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI; 83920K (2012) https://doi.org/10.1117/12.975965
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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.

Citation Download Citation

Ronald Mahler and Adel El-Fallah "An approximate CPHD filter for superpositional sensors", Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 83920K (17 May 2012); https://doi.org/10.1117/12.975965

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