Naslov
Regularized Multitarget Particle Filter for Sensor Management

Autori
El-Fallah, Adel ; Zatezalo, Aleksandar ; Mahler, Ronald ; Mehra, K.Raman ; Alford, Mark

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of SPIE / Kadar, Ivan - Bellingham (WA) : SPIE, 2006

Skup
Defense and Security Symposium 2006

Mjesto i datum
Orlando (FL), Sjedinjene Američke Države, 17.04.2006

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Sensor Management; Multitarget-Multisensor Tracking; Random Sets; Particle Filtering

Sažetak
Sensor management in support of Level 1 data fusion (multisensor integration), or Level 2 data fusion (situation assessment) requires a computationally tractable multitarget filter. The theoretically optimal approach to this multi-target filtering is a suitable generalization of the recursive Bayes nonlinear filter. However, this optimal filter is intractable and computationally challenging that it must usually be approximated. We report on the approximation of a multi-target non-linear filtering for Sensor Management that is based on the particle filter implementation of Stein- Winter probability hypothesis densities (PHDs). Our main focus is on the operational utility of the implementation, and its computational efficiency and robustness for sensor management applications. We present a multitarget Particle Filter (PF) implementation of the PHD that include clustering, regularization, and computational efficiency. We present some open problems, and suggest future developments. Sensor management demonstrations using a simulated multi-target scenario are presented.

Izvorni jezik
Engleski

Znanstvena područja
Matematika

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