Journal article
Multitarget Tracking using Probability Hypothesis Density Smoothing
Abstract
In general, for multitarget problems where the number of targets and their states are time varying, the optimal Bayesian multitarget tracking is computationally demanding. The Probability Hypothesis Density (PHD) filter, which is the first-order moment approximation of the optimal one, is a computationally tractable alternative. By evaluating the PHD, the number of targets as well as their individual states can be extracted. Recent sequential …
Authors
Nadarajah N; Kirubarajan T; Lang T; Mcdonald M; Punithakumar K
Journal
IEEE Transactions on Aerospace and Electronic Systems, Vol. 47, No. 4, pp. 2344–2360
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publication Date
January 1, 2011
DOI
10.1109/taes.2011.6034637
ISSN
0018-9251