A New Probabilistic Data Association Filter Based on Probability Theory

Liu Zong-xiang; Xie Wei-xin; Huang Jing-xiong

doi:10.3724/SP.J.1146.2008.00796

Volume 31 Issue 7

Dec. 2010

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Liu Zong-xiang, Xie Wei-xin, Huang Jing-xiong. A New Probabilistic Data Association Filter Based on Probability Theory[J]. Journal of Electronics & Information Technology, 2009, 31(7): 1641-1645. doi: 10.3724/SP.J.1146.2008.00796

Citation:

Liu Zong-xiang, Xie Wei-xin, Huang Jing-xiong. A New Probabilistic Data Association Filter Based on Probability Theory[J]. Journal of Electronics & Information Technology, 2009, 31(7): 1641-1645. doi: 10.3724/SP.J.1146.2008.00796

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A New Probabilistic Data Association Filter Based on Probability Theory

doi: 10.3724/SP.J.1146.2008.00796

Received Date: 2008-06-23
Rev Recd Date: 2009-03-23
Publish Date: 2009-07-19

Abstract

Abstract

The Probabilistic Data Association Filter (PDAF) and the Joint Probabilistic Data Association Filter (JPDAF) are theoretically analyzed and their shortages in theory are pointed out. Based on the probability theory, a New Probabilistic Data Association Filter (NPDAF) is proposed, in which a measurement may originate from targets or a clutter, but the sum of the probabilities originating from targets and a clutter is equal to 1. Also in the paper, the mathematical model for data association in target tracking and the realization technique for NPDAF are presented. The　correlative probabilities between a measurement and targets are first computed in the realization technique, then the gain of a tracking filter is modified using the correlative probability. Simulation results show that the performance of NPDAF is better than that of JPDAF in multiple target tracking.

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References(1)

References

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