Gaussian mixture importance sampling function for unscented SMC-PHD filter

Yoon, J, Kim, D and Yoon, K 2013, 'Gaussian mixture importance sampling function for unscented SMC-PHD filter', Signal Processing, vol. 93, no. 9, pp. 2664-2670.

Document type: Journal Article
Collection: Journal Articles

Title Gaussian mixture importance sampling function for unscented SMC-PHD filter
Author(s) Yoon, J
Kim, D
Yoon, K
Year 2013
Journal name Signal Processing
Volume number 93
Issue number 9
Start page 2664
End page 2670
Total pages 7
Publisher Elsevier BV
Abstract 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.
Subject Signal Processing
Keyword(s) Gaussian mixture
Importance sampling function
Multitarget filtering
Probability hypothesis density (PHD) filter
Sequential Monte Carlo
DOI - identifier 10.1016/j.sigpro.2013.03.004
Copyright notice © 2013 Elsevier B.V. All rights reserved.
ISSN 0165-1684
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 8 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 33 Abstract Views  -  Detailed Statistics
Created: Thu, 31 Jan 2019, 11:26:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us