CPHD filters for superpositional sensors

Ronald Mahler

doi:10.1117/12.826957

3 September 2009 CPHD filters for superpositional sensors

Ronald Mahler

Proceedings Volume 7445, Signal and Data Processing of Small Targets 2009; 74450E (2009) https://doi.org/10.1117/12.826957
Event: SPIE Optical Engineering + Applications, 2009, San Diego, California, United States

Abstract

The probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters were introduced as approximations of the full multitarget Bayes detection and tracking filter. Both filters are based on the "standard" multitarget measurement model that underlies most multitarget tracking theory. That is, sensor measurements are presumed to be detections. Other sensors collect measurements that are not detections, and among the most important of these are superpositional sensors. A measurement collected by such a sensor is a sum of the real- or complex-valued signals generated by an unknown number of unknown targets present in the scene. This paper describes a theoretical extension of the CPHD filter concept to superpositional sensors.

Citation Download Citation

Ronald Mahler "CPHD filters for superpositional sensors", Proc. SPIE 7445, Signal and Data Processing of Small Targets 2009, 74450E (3 September 2009); https://doi.org/10.1117/12.826957

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