主要利用检测前跟踪动态规划(DP-TBD)算法解决目标跟踪问题。动态规划(DP)是一种先通过对量测空间栅格化处理,然后对离散的量测空间中所有可能的物理路径进行遍历的算法。但是该算法提供的是一种未经滤波的点迹序列。此外,基于单雷达的DP-TBD算法在信噪比(SNR)较低时跟踪效果不佳,航迹丢失情况较严重,因此利用基于DP-TBD的多雷达协同探测势在必行。然而,由于DP-TBD算法没有状态误差协方差矩阵,导致无法将不同雷达的点迹序列进行基于各种融合准则的融合。另外,由于多个雷达不同的采样周期和通信时延,导致了各个雷达的数据是异步的。为了解决以上问题,文中提出了一种基于DP-TBD的分布式异步粒子滤波融合算法(DP-PFF)。该算法分为两步,第一步提出了一种适用于DP算法的粒子滤波方法;第二步是将不同雷达获得的异步状态估计转化为同步的并进行基于DCI准则的分布式融合。仿真结果说明,和单雷达相比,该算法显著提升了目标跟踪的性能。同时,该算法也减少了航迹丢失率并且可以显著提升系统的鲁棒性。

This paper addresses target tracking problems by using multiple radars via dynamic programming (DP) based on track-before-detect (TBD). Generally, DP-TBD is a grid-based method that estimates target trajectories by means of searching all the physically admissible paths in a determinate discrete state space. However, it is a multi-frame detection algorithm which provides plot-sequences without filtering. In addition, single radar based on DP-TBD is unable to achieve satisfactory results when signal-to-noise (SNR) is low. Hence, it is necessary to utilize various radars cooperative detection. Nevertheless, it is really hard to fuse these plot-sequences together in various fusion criterion because they don’t contain state error covariance matrix. What’s more, various radars always own asynchronous data due to diverse sampling time and communication delay. To alleviate these problems, a distributed asynchronous particle filtering fusion (DP-PFF) algorithm based on DP-TBD is proposed in this paper. It is divided into two steps. In the first step, we propose a particle filtering algorithm via DP-TBD. Then, asynchronous evaluation data is converted into synchronous data and DCI fusion criterion is implemented to obtain target state estimation. Simulation results show that the proposed algorithm can correctly estimate target trajectories and significantly enhance the tracking accuracy comparing to solo radar. At the meantime, this algorithm is also able to enhance system robustness.

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