分析了测量方差预先设定对于多传感器融合算法中加权系数分配和状态估计的不利影响，提出了一种测量方差自学习的多传感器加权和滤波算法。该滤波算法能够充分利用传感器每次量测带来新的信息进一步优化测量方差，同时依据优化后测量方差合理地分配权系数和改进状态估计，提高了对状态估计的精度。最后通过仿真计算验证了该算法的有效性。

The influence of the presupposed variance of the measured error on the distribution of weighed coefficient in multi-sensor fusion is analyzed. A new improved multi-sensor weighting and filtering algorithm which is the self-learning of the variance of the measured error is presented. This new algorithm can not only sufficiently utilize renewed information each time from sensor to optimize the variance of the measured error step by step, but also reasonably distributes weighted coefficients to improve the state estimation. Stimulation shows this algorithm can improve significantly the efficiency of maneuvering target tracking.

TN957 TP212

国家自然基金资助项目(60272024)，河南省高校杰出科研人才创新工程项目(2003KYCX003)，河南省高校创新人才培养工程。