| 摘要: |
| 为了提升密集杂波干扰下信度传播多目标跟踪算法的性能,提出基于幅度杂波抑制的高斯混合信度传播多目标跟踪算法.首先基于经典瑞利分布幅度模型,采用极大似然法估计目标初始信噪比,结合先验信息为目标信噪比构造截断正态分布模型,再对其进行边缘化；然后结合幅度信息,在信度传播前计算各量测信息的幅度似然比,并引入量测信息函数中,提高目标与量测的关联正确率；最后设置量测信息录取率,得到幅度似然比下限,对所有传感器的量测信息进行筛选,高效完成目标起始过程.研究表明:在不同信噪比和杂波密度下,通过与GMPHD、GMBP和GMBP-AK算法的对比实验,可以明显发现所提GMBP-AC算法的计算效率较高,能够较准确快速地响应各时间段内目标数目的变化情况,同时大幅度减小OSPA误差.进一步证明在密集杂波干扰环境下,该算法具备较强的杂波抑制效果,且能够较好地改善目标数目估计性能和多目标跟踪精度. |
| 关键词: 多目标跟踪 信度传播 杂波抑制 幅度信息 截断正态分布模型 |
| DOI：10.11918/201812150 |
| 分类号:TN953 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(61271317); 航天支撑技术基金(15GFZ-JJ02-07) |
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| Gaussian mixture belief propagation multi-target tracking under dense clutter |
|
LI Lu,LEI Ming
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(School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China)
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| Abstract: |
| To improve the performance of belief propagation multi-target tracking under dense clutter interference, an amplitude clutter suppression-based Gaussian mixture belief propagation (GMBP-AC) multi-target tracking method was proposed. First, based on the classical Rayleigh distribution model, the initial signal-to-noise ratio (SNR) of target was estimated by the maximum likelihood estimation. The truncated normal distribution model for the SNR of target was constructed with the combination of prior information and then marginalized. Next, based on the amplitude information, the amplitude likelihood ratio (ALR) of each measurement was calculated before belief propagation and introduced into the measurement information function, which improved the association accuracy between targets and measurements. Finally, the measurement information admission rate was set to get the lower limit of ALR, and the measurements of all the sensors were selected to complete the target initiation efficiently. The research shows that under different SNR and clutter densities, compared with GMPHD, GMBP, and GMBP-AK, the proposed GMBP-AC has higher computational efficiency. The method can respond to the changes of target numbers more accurately and quickly in various time periods, and meanwhile reduce the OSPA error greatly. It further proves that under dense clutter interference, the proposed method has high efficiency of clutter suppression, and can improve the target numbers estimation performance and multi-target tracking accuracy. |
| Key words: multi-target tracking belief propagation clutter suppression amplitude information truncated normal distribution model |
