The probabilistic multiple hypothesis tracker (PMHT) uses the expectation-maximization (EM) algorithm to solve the measurement-origin uncertainty problem. Here, we explore some of its variants for maneuvering targets and in particular discuss the multiple model PMHT. We apply this PMHT to the six "typical" tracking scenarios given in the second benchmark problem from W. D. Blair and G. A. Watson (1998). The manner in which the PMHT is used to track the targets and to manage radar allocation is discussed, and the results compared with those of the interacting multiple model probabilistic data association filter (IMM/PDAF) and IMM/MHT (multiple hypothesis tracker). The PMHT works well: its performance lies between those of the IMM/PDAF and IMM/MHT both in terms of tracking performance and computational load.
Multiple model PMHT and its application to the benchmark radar tracking problem
IEEE Transactions on Aerospace and Electronic Systems ; 40 , 4 ; 1337-1350
2004-10-01
929398 byte
Article (Journal)
Electronic Resource
English
Multiple Model PMHT and its Application to the Second Benchmark Radar Tracking Problem
Online Contents | 2004
|British Library Conference Proceedings | 2002
|IEEE | 2004
|IEEE | 2002
|Tracking Algorithm Speed Comparisons Between MHT and PMHT
British Library Conference Proceedings | 2002
|