多目标跟踪的多伯努利平滑方法

为提高基于滤波的多目标跟踪方法的性能,提出了一种多伯努利平滑方法.该方法由前向滤波和反向平滑两部分组成,前向滤波采用势平衡多目标多伯努利滤波,反向平滑利用多伯努利概率密度近似多目标平滑状态的概率密度,实现多目标平滑状态概率密度的反向递推计算.仿真结果表明,与滤波相比,多伯努利平滑对目标数量和目标状态的估计精度都有显著提高.

Multi-Bernoulli smoother for multi-target tracking

In order to improve the capability of multi-target tracking filter, a multi-Bernoulli smoother is proposed. This smoother consists of forward filtering followed by backward smoothing. The forward filtering is accomplished by the cardinality-balanced multi-target multi-Bernoulli filter. For the backward smoothing, the backward recursion of the smoothed multi-target probability density is achieved by using multi-Bernoulli approximation. Simulation results show that the proposed smoother improves the estimation accuracy of target number and target states over the filter.