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dc.contributor.authorYoon, J.
dc.contributor.authorKim, Du Yong
dc.contributor.authorYoon, K.
dc.identifier.citationYoon, J. and Kim, D.Y. and Yoon, K. 2013. Gaussian mixture importance sampling function for unscented SMC-PHD filter. Signal Processing. 93 (9): pp. 2664-2670.

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. © 2013 Elsevier B.V. All rights reserved.

dc.publisherElsevier BV
dc.titleGaussian mixture importance sampling function for unscented SMC-PHD filter
dc.typeJournal Article
dcterms.source.titleSignal Processing
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available

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