.png){kind=icon}
.png){kind=icon}
Return
Multitarget Filtering With Unknown Clutter Density Using a Bootstrap GMCPHD Filter
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2013.2244594.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Adaptive filtering
clutter rate estimation
multitarget filtering
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W