基于DP-TBD的分布式异步迭代滤波融合算法研究

该文主要运用检测前跟踪动态规划(Dynamic Programming-Track Before Detect)算法解决目标跟踪问题。动态规划(Dynamic Programming, DP)是一种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。然而，该算法提供的是一种未经滤波和平滑的点迹序列。随着实际战争环境日益复杂，基于单雷达的DP-TBD算法在信噪比(SNR)较低时跟踪效果不佳。此外，由于DP-TBD算法没有状态误差协方差矩阵，因此无法将不同雷达的点迹序列进行融合。而且由于通信时延和不同的采样周期，不同雷达的数据往往是异步的。为了解决以上问题，该文提出了一种基于DP-TBD的分布式异步迭代滤波融合算法(DynamicProgramming?Fuison, DPF)。该算法分为两步，第1步提出了一种迭代滤波方法对DP点迹进行处理；第2步将不同雷达获得的异步状态估计转化为同步的，接着利用几种分布式的融合方法来获取融合之后的状态估计。仿真结果说明，和单雷达相比，该融合算法可以有效提升目标跟踪的性能，同时，该算法也可以降低航迹丢失率和计算量。 

A Distributed Asynchronous Recursive Filtering Fusion Algorithm via DP-TBD

In this paper, we address target tracking problems by the use of multiple sensors via the Dynamic Programming (DP)-based Track-Before-Detect (TBD) method. Generally, DP-TBD is a grid-based method that estimates target trajectories by searching all the physically admissible paths in a determinate discrete state space. However, this multi-frame detection algorithm provides plot sequences without filtering or smoothing. With the growing complexity of the battle field environment, single radar based on DP-TBD cannot achieve satisfactory results when the Signal-to-Noise Ratio (SNR) is low. Besides, it is very difficult to fuse plot sequences from different radars because they contain no state error covariance matrix. Furthermore, various radars always contain asynchronous data due to the diversity of sampling times and communication delays. To alleviate these problems, we propose a distributed asynchronous recursive filtering fusion (Dynamic Programming Fuison, DPF) algorithm based on DP-TBD, which is divided into two steps. In the first step, we propose an iterative filter algorithm via DP-TBD. Then, we convert the asynchronous evaluation data into synchronous data and implement several distributed fusion algorithms to estimate the target state. Simulation results show that the proposed algorithm can correctly estimate target trajectories and significantly enhance tracking accuracy compared to solo radar. In addition, this algorithm can decrease the track loss rate and calculation burden.