He Xiangyu, Liu Guixi
School of Mechano-electronic Engineering, Xidian University, Xi'an 710071, China.
School of Physics and Electronic Information, Luoyang Normal University, Luoyang 471934, China.
Sensors (Basel). 2016 Aug 31;16(9):1399. doi: 10.3390/s16091399.
The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter developed recently has been proved an effective multi-target tracking (MTT) algorithm based on the random finite set (RFS) theory, and it can jointly estimate the number of targets and their states from a sequence of sensor measurement sets. However, because of the existence of systematic errors in sensor measurements, the CBMeMBer filter can easily produce different levels of performance degradation. In this paper, an extended CBMeMBer filter, in which the joint probability density function of target state and systematic error is recursively estimated, is proposed to address the MTT problem based on the sensor measurements with systematic errors. In addition, an analytic implementation of the extended CBMeMBer filter is also presented for linear Gaussian models. Simulation results confirm that the proposed algorithm can track multiple targets with better performance.
最近开发的基数平衡多目标多伯努利（CBMeMBer）滤波器已被证明是一种基于随机有限集（RFS）理论的有效多目标跟踪（MTT）算法，它可以从一系列传感器测量集中联合估计目标数量及其状态。然而，由于传感器测量中存在系统误差，CBMeMBer滤波器很容易产生不同程度的性能下降。本文提出了一种扩展的CBMeMBer滤波器，其中递归估计目标状态和系统误差的联合概率密度函数，以解决基于存在系统误差的传感器测量的MTT问题。此外，还针对线性高斯模型给出了扩展CBMeMBer滤波器的解析实现。仿真结果证实，所提出的算法能够以更好的性能跟踪多个目标。
