基于有偏卡尔曼的多传感器数据融合研究

推导出偏参数为矩阵形式的有偏卡尔曼滤波(BKF)的完整迭代过程,该算法在均方误差条件下优于卡尔曼滤波(KF),可以进一步提高估计的精度.将BKF与多传感器融合算法中的扩维融合和序贯式融合相结合,推导出多传感器扩维有偏卡尔曼滤波和多传感器序贯有偏卡尔曼滤波算法,并从理论上证明了多传感器序贯BKF融合在均方误差条件下优于扩...

Research on Multi-sensor Data Fusion Based on Biased Kalman

The complete iterative procedure of the biased Kalman filter(BKF)with bias parameters in matrix form was derived,which outperforms the Kalman filter(KF)under the mean square error condition and can further improve the accuracy of estimation.By combining the BKF with the extended dimensional fusion and sequential fusion in the multi-sensor fusion algorithm,the multi-sensor extended dimensional biased Kalman filter and multi-sensor sequential biased Kalman filter algorithms can be derived.And it is theoretically demonstrated that multi-sensor sequential BKF fusion outperforms expanded dimensional BKF fusion under the mean square error condition.The simulation experimental results show that both multi-sensor extended dimensional BKF fusion and sequential BKF fusion are better than extended dimensional KF fusion as well as sequential KF fusion,and sequential BKF fusion is better than extended dimensional BKF fusion under the mean square error condition.