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Particle flow for sequential Monte Carlo implementation of probability hypothesis density

Liu, Yang, Wang, Wenwu and Zhao, Y (2017) Particle flow for sequential Monte Carlo implementation of probability hypothesis density In: 42nd IEEE ICASSP, 2017-03-05 - 2017-03-09, New Orleans, USA.

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Abstract

Target tracking is a challenging task and generally no analytical solution is available, especially for the multi-target tracking systems. To address this problem, probability hypothesis density (PHD) filter is used by propagating the PHD instead of the full multi-target posterior. Recently, the particle flow filter based on the log homotopy provides a new way for state estimation. In this paper, we propose a novel sequential Monte Carlo (SMC) implementation for the PHD filter assisted by the particle flow (PF), which is called PF-SMCPHD filter. Experimental results show that our proposed filter has higher accuracy than the SMC-PHD filter and is computationally cheaper than the Gaussian mixture PHD (GM-PHD) filter.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Liu, Yangyangliu@surrey.ac.ukUNSPECIFIED
Wang, WenwuW.Wang@surrey.ac.ukUNSPECIFIED
Zhao, YUNSPECIFIEDUNSPECIFIED
Date : 19 June 2017
Identification Number : 10.1109/ICASSP.2017.7952982
Copyright Disclaimer : (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Contributors :
ContributionNameEmailORCID
UNSPECIFIEDIEEE, UNSPECIFIEDUNSPECIFIED
Uncontrolled Keywords : tracking system, SMC-PHD, particle flow, multi-target
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 14 Feb 2017 17:59
Last Modified : 06 Sep 2017 09:25
URI: http://epubs.surrey.ac.uk/id/eprint/813537

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