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Mathematical Problems in Engineering
Volume 2015, Article ID 629023, 9 pages
Research Article

GM-PHD Filter Combined with Track-Estimate Association and Numerical Interpolation

1School of Computer Science, Xi’an Polytechnic University, Xi’an 710048, China
2School of Human Ecology, University of Texas at Austin, Austin, TX 78712, USA

Received 29 April 2015; Revised 19 June 2015; Accepted 21 June 2015

Academic Editor: Muhammad N. Akram

Copyright © 2015 Jinguang Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


For the standard Gaussian mixture probability hypothesis density (GM-PHD) filter, the number of targets can be overestimated if the clutter rate is too high or underestimated if the detection rate is too low. These problems seriously affect the accuracy of multitarget tracking for the number and the value of measurements and clutters cannot be distinguished and recognized. Therefore, we proposed an improved GM-PHD filter to tackle these problems. Firstly, a track-estimate association was implemented in the filtering process to detect and remove false-alarm targets. Secondly, a numerical interpolation technique was used to compensate the missing targets caused by low detection rate. At the end of this paper, simulation results were presented to demonstrate the proposed GM-PHD algorithm is more effective in estimating the number and state of targets than the previous ones.