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| Gaussian Particle JPDA Filter Based Multi-target Tracking |
| Zhang Jun-gen Ji Hong-bing Cai Shao-xiao |
| School of Electronic Engineering, Xidian University, Xi’an 710071, China |
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Abstract In multi-target tracking, aiming at the data association problem that arises due to indistinguishable measurements in the presence of clutter, and the curse of dimensionality that arises due to the increased size of the state-space associated with multiple targets, a novel algorithm based on Gaussian Particle Joint Probabilistic Data Association Filter (GP-JPDAF) is proposed, which introduces Gaussian Particle Filtering (GPF) concept to the JPDA framework. For each of the targets, the marginal association probabilities are approximated with Gaussian particles rather than Gaussians in the JPDAF. Moreover, GPF is utilized for approximating the prediction and update distributions. Finally, the proposed method is applied to passive multi-sensor multi-target tracking. Simulation results show that the method can obtain better tracking performance than Monte Carlo JPDAF (MC -JPDAF).
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Received: 04 December 2009
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Corresponding Authors:
Zhang Jun-gen
E-mail: zhang_jungen@sina.com
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2010, Vol. 32 
