基于杂波量测集约束的改进MS-MeMBer滤波器

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。 

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

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.