Multitarget Filtering With Unknown Clutter Density Using a Bootstrap GMCPHD Filter
It was recently demonstrated that the Gaussian Mixture Cardinalised Probability Hypothesis Density (GMCPHD) filter can be used when the clutter density is unknown. Here we examine the performance of this filter, and as one would expect, it does not do as well as the conventional GMCPHD with matched...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Institute of Electrical and Electronics Engineers
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/27541 |
| Summary: | It was recently demonstrated that the Gaussian Mixture Cardinalised Probability Hypothesis Density (GMCPHD) filter can be used when the clutter density is unknown. Here we examine the performance of this filter, and as one would expect, it does not do as well as the conventional GMCPHD with matched clutter density. To improve the performance, we propose a bootstrap filtering scheme, and demonstrate by simulations on a bearings-only multitarget filtering scenario, that it is capable of performing almost as well as the matched GMCPHD filter. |
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