基于多传感器抗差融合的UKF弹道跟踪算法

弹道跟踪测量过程中,由于环境的复杂性和测量机制自身的问题,测量数据不可避免存在异常值等.传统的加权观测融合估计算法往往直接对来自各个传感器的测量数据进行处理,忽略了数据质量问题对滤波精度的影响.为解决此问题,在加权观测融合算法的基础上引入抗差估计理论,根据观测融合值与融合预测值,计算测量融合残差向量、抗差权重因子和融合观测向量等价协方差阵,实现了异常值的实时分离与修正,解决了融合过程中由于测量数据存在污染导致弹道数据处理精度下降的问题.同时引入平方根滤波思想,避免了常规UKF中误差协方差矩阵非正值引起的滤波散度问题.仿真结果表明该算法估计精度高,计算负担小,能有效地减小测量误差对弹道定轨精度的影响.

A UKF Trajectory Tracking Algorithm Based on Multi-Sensor Robust Fusion

In the process of trajectory tracking measurement, the abnormal values of measurement data are in existence inevitably due to the complexity of the environment and the limitation of the measurement mechanism itself. Being ignorance from the influence of data quality on filtering accuracy, the traditional weighted observation fusion estimation algorithms are often directly used to deal with the measurement data from various sensors. For this reason, the robust estimation theory is introduced into the weighted observation fusion algorithm, and the measurement fusion residual vector, the robust weight factor and the equivalent covariance matrix of the fusion observation vector are calculated based on the observation fusion value and the fusion prediction value. The algorithm proposed realizes the real time separation and correction of abnormal values, and solves the accuracy decreasing problem of the ballistic data fusion estimation due to the pollution of the measurement data. At the same time, the square root filtering idea is introduced, avoiding the filtering divergence problem caused by the non positive value of the error covariance matrix in the conventional UKF. The simulation results show that the fusion algorithm is high in estimation accuracy and has a little burden to computation, and can effectively reduce the influence of measurement errors on the accuracy of ballistic orbit determination.