Performance Evaluation of Nonlinear Filters for Tracking Multiple Ballistic Targets (CROSBI ID 525370)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Sinhaa, A. ; Nandakumarana, N. ; Sutharsana, S. ; Kirubarajana, T. ; El-Fallah, Adel ; Zatezalo, Aleksandar
engleski
Performance Evaluation of Nonlinear Filters for Tracking Multiple Ballistic Targets
The particle filter is an effective technique for target tracking in the presence of nonlinear system model, nonlinear measurement model or non-Gaussian noise in the system and/or measurement processes. In this paper, we compare three particle filtering algorithms on a spawning ballistic target tracking scenario. One of the algorithms, the tagged particle filter (TPF), was recently developed by us. It uses separate sets of particles for separate tracks. However, data association to different tracks is interdependent. The other two algorithms implemented in this paper are the probability hypothesis density (PHD) algorithm and the joint multitarget probability density (JMPD). The PHD filter propagates the first order statistical moment of multitarget density using particles. While, the JMPD stacks the states of a number of targets to form a single particle that is representative of the whole system. Simulation results are presented to compare the performances of these algorithms.
closely-spaced target tracking; spawning target tracking; feature-aided tracking; particle filters
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Podaci o prilogu
2005.
objavljeno
Podaci o matičnoj publikaciji
Podaci o skupu
Defense and Security Symposium 2005
predavanje
28.03.2005-28.03.2005
Orlando (FL), Sjedinjene Američke Države