北京航空航天大学学报 ›› 2021, Vol. 47 ›› Issue (9): 1748-1755.doi: 10.13700/j.bh.1001-5965.2020.0317

• 论文 • 上一篇    下一篇

基于杂波量测集约束的改进MS-MeMBer滤波器

陆小科1, 张志国2, 孙进平2, 孙伟1   

  1. 1. 南京电子技术研究所, 南京 210039;
    2. 北京航空航天大学 电子信息工程学院, 北京 100083
  • 收稿日期:2020-07-03 发布日期:2021-10-09
  • 通讯作者: 孙进平 E-mail:sunjinping@buaa.edu.cn
  • 基金资助:
    国家自然科学基金(61471019)

An improved multi-sensor MeMBer filter based on clutter measurement set constraint

LU Xiaoke1, ZHANG Zhiguo2, SUN Jinping2, SUN Wei1   

  1. 1. Nanjing Research Institute of Electronics Technology, Nanjing 210039, China;
    2. School of Electronic and Information Engineering, Beihang University, Beijing 100083, China
  • Received:2020-07-03 Published:2021-10-09
  • Supported by:
    National Natural Science Foundation of China (61471019)

摘要: 针对高杂波密度场景下,传统多传感器多目标多伯努利(MS-MeMBer)滤波器存在的量测划分假设质量下降、势估计结果出现偏差等问题,提出了一种基于杂波量测集约束的改进MS-MeMBer滤波器。首先,通过将杂波量测集的影响引入到更新过程中,优化了目标量测集的权重,并给出了杂波场景下的单目标多传感器似然函数。然后,通过两步贪婪划分机制,得到了改进的多传感器量测划分假设。通过仿真将所提方法与传统MS-MeMBer滤波器进行了比较,实验结果表明:在高杂波密度场景下,改进MS-MeMBer滤波器具有更优的多目标跟踪性能。

关键词: 多目标跟踪, 多传感器多目标多伯努利(MS-MeMBer)滤波器, 杂波量测集, 量测划分假设, 高杂波密度

Abstract: To solve the problems existing in the traditional Multi-Sensor Multi-Target Multi-Bernoulli (MS-MeMBer) filter in the high clutter density scene, such as poor quality of measurement partitioning hypothesis and biases of cardinality estimation, an improved MS-MeMBer filter based on clutter measurement set constraint is proposed. By introducing the influence of the clutter measurement set into the update step, the weight of the target measurement set is optimized and the single target multi-sensor likelihood function in the clutter scene is given. After that, the improved multi-sensor measurement partitioning hypothesis is obtained by two-step greedy partition mechanism. The proposed method is compared with the traditional MS-MeMBer filter by simulation. The experimental results show that the proposed method has better multi-target tracking performance in high clutter density scene.

Key words: multi-target tracking, Multi-Sensor Multi-Target Multi-Bernoulli (MS-MeMBer) filter, clutter measurement set, measurement partitioning hypothesis, high clutter density

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