.png){kind=icon}
.png){kind=icon}
Return
Gaussian mixture importance sampling function for unscented SMC-PHD filter
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2013.03.004.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The unscented sequential Monte Carlo probability hypothesis density (USMC-PHD) filter has been proposed to improve the accuracy performance of the bootstrap SMC-PHD filter in cluttered environments. However, the USMC-PHD filter suffers from heavy computational complexity because the unscented information filter is assigned for every particle to approximate an importance sampling function. In this paper, we propose a Gaussian mixture form of the importance sampling function for the SMC-PHD filter to considerably reduce the computational complexity without performance degradation. Simulation results support that the proposed importance sampling function is effective in computational aspects compared with variants of SMC-PHD filters and competitive to the USMC-PHD filter in accuracy. (c) 2013 Elsevier B.V. All rights reserved.
Keywords:
Multitarget filtering
Probability hypothesis density (PHD) filter
Importance sampling function
Sequential Monte Carlo
Gaussian mixture
Journal
IF:
3.6
Papers:
9.9K
Citations:
1.7W