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Multi-Target Tracking Using an Improved Gaussian Mixture CPHD Filter

College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
Author to whom correspondence should be addressed.
Academic Editor: Stefano Mariani
Sensors 2016, 16(11), 1964;
Received: 21 August 2016 / Revised: 29 October 2016 / Accepted: 17 November 2016 / Published: 23 November 2016
(This article belongs to the Section Physical Sensors)
The cardinalized probability hypothesis density (CPHD) filter is an alternative approximation to the full multi-target Bayesian filter for tracking multiple targets. However, although the joint propagation of the posterior intensity and cardinality distribution in its recursion allows more reliable estimates of the target number than the PHD filter, the CPHD filter suffers from the spooky effect where there exists arbitrary PHD mass shifting in the presence of missed detections. To address this issue in the Gaussian mixture (GM) implementation of the CPHD filter, this paper presents an improved GM-CPHD filter, which incorporates a weight redistribution scheme into the filtering process to modify the updated weights of the Gaussian components when missed detections occur. In addition, an efficient gating strategy that can adaptively adjust the gate sizes according to the number of missed detections of each Gaussian component is also presented to further improve the computational efficiency of the proposed filter. Simulation results demonstrate that the proposed method offers favorable performance in terms of both estimation accuracy and robustness to clutter and detection uncertainty over the existing methods. View Full-Text
Keywords: GM-CPHD filter; multi-target tracking; spooky effect; weight redistribution GM-CPHD filter; multi-target tracking; spooky effect; weight redistribution
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Si, W.; Wang, L.; Qu, Z. Multi-Target Tracking Using an Improved Gaussian Mixture CPHD Filter. Sensors 2016, 16, 1964.

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