Abstract: Aiming at the problem of fixed interval smoothing in multi-target tracking, the Cardinalized Probability Hypothesis Density（CPHD） filter and the RTS smoother are combined, and a cardinalized probability hypothesis density smoothing algorithm for RTS is given. Considering the problem of large output delay in the smoothing process, a piecewise RTS cardinalized probability hypothesized density smoother is proposed using the idea of piecewise smoothing. Firstly, estimation values are segmented using a fixed interval. Secondly, track-estimate is associated using Hungarian algorithm. Finally, the RTS smoothing is performed on the associated tracks. The experimental results show, the CPHD smoother using piecewise RTS can estimate the target state more accurately comparing with the CPHD filter, and can effectively avoid the problem of poor real-time performance when used RTS smoother directly.

Key words: target tracking, RTS smoother, Cardinalized Probability Hypothesis Density filter（CPHD）, track-estimate association, information fusion

摘要： 针对多目标跟踪中的固定间隔平滑问题，将势概率假设密度（CPHD）滤波器和RTS平滑器相结合，提出了RTS的势概率假设密度滤波平滑算法。考虑到在平滑过程中存在较大的输出延迟问题，采用分段思想，提出了分段RTS的势概率假设密度滤波平滑算法。对需要平滑的估计值进行分段；采用匈牙利算法进行航迹-估计关联；对关联后的估计值逐段进行RTS平滑。实验结果表明，与CPHD滤波结果相比，分段RTS的势概率假设密度滤波平滑算法能够更加精确地估计目标状态，并且可以有效避免直接应用RTS平滑造成的实时性欠佳问题。

关键词: 目标跟踪, RTS平滑, 势概率假设密度滤波, 航迹-估计关联, 信息融合