Regularized Multitarget Particle Filter for Sensor Management (CROSBI ID 525372)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
El-Fallah, Adel ; Zatezalo, Aleksandar ; Mahler, Ronald ; Mehra, K.Raman ; Alford, Mark
engleski
Regularized Multitarget Particle Filter for Sensor Management
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.
Sensor Management; Multitarget-Multisensor Tracking; Random Sets; Particle Filtering
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
2006.
objavljeno
Podaci o matičnoj publikaciji
Podaci o skupu
Defense and Security Symposium 2006
predavanje
17.04.2006-17.04.2006
Orlando (FL), Sjedinjene Američke Države