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Open AccessArticle

Cardinality Balanced Multi-Target Multi-Bernoulli Filter with Error Compensation

by Xiangyu He 1,2 and Guixi Liu 1,*
1
School of Mechano-electronic Engineering, Xidian University, Xi’an 710071, China
2
School of Physics and Electronic Information, Luoyang Normal University, Luoyang 471934, China
*
Author to whom correspondence should be addressed.
Academic Editors: Xue-Bo Jin, Feng-Bao Yang, Shuli Sun and Hong Wei
Sensors 2016, 16(9), 1399; https://doi.org/10.3390/s16091399
Received: 20 June 2016 / Revised: 15 August 2016 / Accepted: 25 August 2016 / Published: 31 August 2016
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter developed recently has been proved an effective multi-target tracking (MTT) algorithm based on the random finite set (RFS) theory, and it can jointly estimate the number of targets and their states from a sequence of sensor measurement sets. However, because of the existence of systematic errors in sensor measurements, the CBMeMBer filter can easily produce different levels of performance degradation. In this paper, an extended CBMeMBer filter, in which the joint probability density function of target state and systematic error is recursively estimated, is proposed to address the MTT problem based on the sensor measurements with systematic errors. In addition, an analytic implementation of the extended CBMeMBer filter is also presented for linear Gaussian models. Simulation results confirm that the proposed algorithm can track multiple targets with better performance. View Full-Text
Keywords: error compensation; multi-target multi-Bernoulli filter; multi-target tracking; random finite set error compensation; multi-target multi-Bernoulli filter; multi-target tracking; random finite set
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He, X.; Liu, G. Cardinality Balanced Multi-Target Multi-Bernoulli Filter with Error Compensation. Sensors 2016, 16, 1399.

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