测量方差自适应的多传感器数据融合算法

针对测量方差预先设定对滤波精度的影响，提出了一种基于时空综合分析在线估算测量方差的方法，以充分利用多个传感器单次采样数据和单个传感器多次采样数据中蕴含的冗余和互补信息。改善了由于被测目标所处环境的先验信息匮乏而造成的建模误差问题。同时根据滤波误差协方差阵具有表示滤波精度的特性，实现系统中各传感器权系数动态合理的分配。最终给出了一种基于测量方差自适应的多传感器数据融合算法。通过蒙特卡罗仿真验证了该算法的有效性。

Algorithm of multi-sensors data fusion based on the variance of the measured error adaptive

The influence of the variance of the presupposed measured error is analyzed briefly. A new method is presented on the basis of the on-line estimation for the variance of the measured error through spatial-temporal synthetical analysis. It can make full use of redundancy and complementary in- formation, which comes from the single sampled data of multi-sensors and the multi-times sampled data of single sensor, thereby, which improves the influence of modeling error caused by the shortage of prior information for the condition of observed. At the same time, utilizing the characteristic that filtering error covariance expresses filtering precision, the dynamic and reasonable distribution of the every sensor weight coefficient is accomplished. Finally,a new algorithm of multi-sensors fusion based on the variance of the measured error adaptive is given.Monte-Carlo simulation shows that this algorithm can obviously improve the efficiency of maneuvering target tracking.