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

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