Gaussian Particle JPDA Filter based multi-target tracking

被引：0
|
作者：
Zhang J.-G. [1 ]
Ji H.-B. [1 ]
Cai S.-X. [1 ]
机构：
[1] School of Electronic Engineering, Xidian University
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2010年 / 32卷 / 11期
关键词：
Gaussian Particle Filtering (GPF); Joint Probabilistic Data Association (JPDA); Multi-target tracking; Passive multi-sensor;
D O I：
10.3724/SP.J.1146.2009.01549
中图分类号：
学科分类号：
摘要：
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).
引用
收藏
页码：2686 / 2690
页数：4
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