可处理任意步延迟OOSMs的最优更新算法

Optimal Updating Algorithm for Processing Out-of-Sequence Measurements with Any Steps of Delay

在目标跟踪系统中,因通信延迟会出现传感器量测无序到达融合中心的现象,从而产生无序量测问题。而在系统实际工作过程中,面临的常常是多个无序量测问题。针对此问题,对其常见情形进行分类,在前向预测框架内提出了Dl算法。该算法对无序量测融合周期内各时刻状态估计与协方差矩阵进行更新,可处理任意步延迟无序量测。此外,针对其中单个无序量测,推导出融入等价量测的信息滤波器,无需求状态转移矩阵的逆,且不依赖过程噪声离散化模型。仿真结果验证了算法的精确性与有效性。

In target tracking system, the measurements from different sensors might arrive at the fusion center out of sequence because of the different communication delays, and thus result in the Out-of-Sequence Measurement （OOSM） problem. Usually there are multiple OOSMs in the actual working process of the system. Aiming at this problem, the common situation is classified, and D1 algorithm is proposed within the forward prediction filtering framework. The algorithm can update the state estimation and the covariance matrix on each moment of OOSM fusion period, and deal with multiple OOSMs with arbitrary steps of delay. In addition, the equivalent measurement information filter is deduced for single OOSM without requiring the inverse of the state transition matrix and the discrete model of process noise. Simulations verify the precision and effectiveness of the proposed algorithm.