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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Clark, D.E. Ba Tuong Vo Ba Ngu Vo |
| Copyright Year | 2009 |
| Description | Author affiliation: Department of Electrical & Electronic Engineering, The University of Melbourne, Parkville VIC 3052, Australia (Ba Ngu Vo) || Joint Research Institute in Signal & Image Processing, School of Engineering & Physical Sciences, Heriot-Watt University, UK (Clark, D.E.) || School of Electrical, Electronic & Computer Engineering, The University of Western Australia, 35 Stirling Hwy, Crawley, 6009, Australia (Ba Tuong Vo) |
| Abstract | The problem of jointly detecting whether a target is present in a scene and, if there is, estimating its state can be viewed as a multi-object estimation problem where there is a maximum of one target. This joint detection and estimation problem can be solved using a special case of the multi-object Bayes filter. In this paper we investigate the joint target detection and estimation problem with forward-backward smoothing and propose a sequential Monte Carlo implementation. Finite Set Statistics not only facilitates the development of appropriate joint detection and estimation filters, but also the direct extension of these filtering solutions to their related smoothing counterparts. Preliminary results indicate that using the smoothing has two distinct advantages over just using filtering: Firstly, we are able to more accurately identify the appearance and disappearance of a target in the scene and secondly, we can provide improved state estimates when the target exists. |
| Starting Page | 899 |
| Ending Page | 906 |
| File Size | 842189 |
| Page Count | 8 |
| File Format | |
| ISBN | 9780982443804 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-07-06 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | ISIF |
| Subject Keyword | Monte Carlo methods Smoothing methods Object detection Target tracking Nonlinear filters Filtering State estimation Layout Current measurement Delay estimation smoothing Tracking filtering detection estimation |
| Content Type | Text |
| Resource Type | Article |
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