Statistics – Applications
Scientific paper
2011-08-30
Statistics
Applications
22 pages
Scientific paper
In radar systems, tracking targets in low signal-to-noise ratio (SNR) environments is a very important task. There are some algorithms designed for multitarget tracking. Their performances, however, are not satisfactory in low SNR environments. Track-before-detect (TBD) algorithms have been developed as a class of improved methods for tracking in low SNR environments. However, multitarget TBD is still an open issue. In this paper, multitarget TBD measurements are modeled, and a highly efficient filter in the framework of finite set statistics (FISST) is designed. Then, the probability hypothesis density (PHD) filter is applied to multitarget TBD. Indeed, to solve the problem of the target and noise not being separated correctly when the SNR is low, a shrinkage-PHD filter is derived, and the optimal parameter for shrinkage operation is obtained by certain optimization procedures. Through simulation results, it is shown that our method can track targets with high accuracy by taking advantage of shrinkage operations.
Meng Huadong
Tong Huisi
Wang Xiqin
Zhang Hao
No associations
LandOfFree
A shrinkage probability hypothesis density filter for multitarget tracking does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with A shrinkage probability hypothesis density filter for multitarget tracking, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A shrinkage probability hypothesis density filter for multitarget tracking will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-729012