目标跟踪中野值的判别与剔除方法

摘要

When Kalman filter is applied to target tracking, the outliers in dynamic measurement data are one of the important factors which affect filtering performance. The reasons and characteristics of outliers emerging in the dynamic measurement data are analyzed, as well as the mechanism that outliers affect Kalman filter. Based on the statistical characteristics of innovation, the method of dynamically identifying outliers and anti-outlier Kalman filter algorithm are presented. The simulation results demonstrate that the method can eliminate outliers and estimate states simultaneously, and it can effectively eliminate the negative effect of the outlier on the filter, and improve the filter accuracy.%运用 Kalman滤波对目标位置进行跟踪时，测量数据中的野值是影响滤波效果的重要因素之一。分析了动态测量数据中野值产生的原因和野值对 Kalman滤波性能的影响机理，利用新息的统计特性，提出了野值判别准则和一种抗野值 Kalman滤波算法。仿真结果表明，该方法使野值处理与状态估计同步进行，可有效抑制野值对 Kalman滤波的影响，提高滤波精确度。