基于高斯混合势化概率假设密度的脉冲多普勒雷达多目标跟踪算法

吴卫华; 江晶; 冯讯; 刘重阳

doi:10.11999/JEIT141232

基于高斯混合势化概率假设密度的脉冲多普勒雷达多目标跟踪算法

doi: 10.11999/JEIT141232

基金项目:

国家自然科学基金(61102168)资助课题

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- 文章访问数: 1488
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出版历程
- 收稿日期: 2014-09-23
- 修回日期: 2014-12-15
- 刊出日期: 2015-06-19

Multi-target Tracking Algorithm Based on GaussianMixture Cardinalized Probability Hypothesis

摘要

摘要: 为在新兴的随机有限集(RFS)框架下充分利用多普勒信息跟踪杂波环境下的多目标，该文提出基于高斯混合势化概率假设密度(GM-CPHD)的脉冲多普勒雷达多目标跟踪(MTT)算法。该算法在标准GM-CPHD基础上，在使用位置量测更新状态后，再利用多普勒量测进行序贯更新，可获得更精确的似然函数和状态估计。仿真结果验证了该算法的有效性，表明在GM-CPHD基础上引入目标的多普勒信息可有效抑制杂波，显著改善跟踪性能。
- 多目标跟踪 /
- 随机有限集 /
- 概率假设密度 /
- 高斯混合势化概率假设密度 /
- 脉冲多普勒雷达
Abstract: In order to take full advantage of Doppler information for Multi-Target Tracking (MTT) in the clutter environment under the framework of emerging Random Finite Sets (RFS), an MTT algorithm based on Gaussian Mixture Cardinalized Probability Hypothesis Density (GM-CPHD) for pulse Doppler radar is proposed. Based on the standard GM-CPHD, the target states are updated sequentially using Doppler measurements after updating them using position measurements, then more accurate likelihood function and state estimation are obtained. Simulation results show the effectiveness of the proposed algorithm, and the introduced Doppler information can effectively suppress clutter and evidently improve tracking performance.

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参考文献(16)

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