Cardinality Balanced Multi-Target Multi-Bernoulli Filter with Error CompensationReport as inadecuate


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


Cardinality Balanced Multi-Target Multi-Bernoulli Filter with Error Compensation - Download this document for free, or read online. Document in PDF available to download.

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

Abstract 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





Author: Xiangyu He 1,2 and Guixi Liu 1,*

Source: http://mdpi.com/



DOWNLOAD PDF




Related documents