基于选择融合的任意步滞后无序量测滤波算法

Filtering Algorithm on Arbitrary-Step-Lag Out-of-Sequence Measurement Based on Selective Fusion

在目标跟踪系统中,由于传感器具有不同的预处理时间与采样速率,以及信道固有的随机通信延迟,传感器量测数据可能出现无序到达融合中心的现象,即无序量测问题。在系统工作过程中,通常有多个无序量测相继或同时出现。为此,将多无序量测情形进行分类,基于选择融合提出任意步滞后无序量测滤波算法。利用基于对数似然比的假设检验筛选出需要处理的无序量测。在前向预测框架内,根据无序量测最优滤波过程,采用融入等价量测的信息滤波方法对目标状态估计与误差协方差矩阵进行更新。仿真结果验证了算法的精确性与有效性。

In target tracking system,because the sensor has different preprocessing time and sampling rate,and the inherent random communication delay of the channel,the phenomenon of random arrival of the fusion center may appear in the sensor data,that is,the problem of disorder measurement.In the process of system operation,there are usually a number of Out-of-Sequence Measurement（OOSM） appearing in succession or at the same time.Aiming at this problem,classifying multiple disorder measurements,a filtering algorithm on arbitrary-step-lag out-of-sequence measurements based on selective fusion is put forward.The algorithm uses log likelihood ratio hypothesis test to choose the out-of-sequence measurements.Then,according to the optimal OOSM filtering process,the state estimation and the covariance matrix with the information filtering method blended in equivalent measurement within the forward prediction framework is updated.Simulation results verify the precision and effectiveness of the proposed algorithm.