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Content Provider | IET Digital Library |
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Author | Li, Quan rui Qi, Bin Liang, Guo long |
Abstract | An approximate Bayes multi-target tracking (MTT) smoother that is directly applicable to MTT is proposed in this study. By using labelled random finite set, the proposed smoother inherently produced trajectories of the targets. The smoother is based on Bayes forward–backward smoothing scheme, which involves forward filtering followed by backward smoothing. It is shown that if the multi-target transition kernel involved in the backward smoothing stage considers no birth and death of targets, then the smoothing density is of the same form as the filtering density. To avoid the exponential complexity of computation, an efficient approximation that utilises the m-best S-D assignment algorithm to truncate the smoothing density is presented. A Gaussian mixture implementation of the smoother for a linear system is also given. Simulation results in MTT scenarios demonstrate that the smoother outperforms generalised labelled multi-Bernoulli filter in terms of optimal sub-pattern assignment metric. |
Starting Page | 428 |
Ending Page | 437 |
Page Count | 10 |
ISSN | 17518784 |
Volume Number | 13 |
e-ISSN | 17518792 |
Issue Number | Issue 3, Mar (2019) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-rsn/13/3 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2018.5297 |
Journal | IET Radar, Sonar & Navigation |
Publisher Date | 2018-11-01 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Approximate Bayes Multitarget Tracking Smoother Backward Smoothing Stage Bayes Method Combinatorial Mathematics Efficient Approximation Exponential Complexity Filtering Density Filtering Method in Signal Processing Filtering Theory Forward–backward Smoothing Scheme Gaussian Mixture Implementation Gaussian Processes Labelled Random Finite Set M-best S-D Assignment Algorithm Mixture Model MTT Scenario MultiBernoulli Filter Multitarget Transition Kernel Set Theory Signal Processing Theory Smoothing Density Smoothing Method Statistics Target Tracking Tracking Filter |
Content Type | Text |
Resource Type | Article |
Subject | Electrical and Electronic Engineering |
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