关键词: 多传感器数据融合, 增量卡尔曼滤波, 扩维融合, 序贯融合, 量测系统误差

Abstract: In the state estimation system of single sensor, standard incremental Kalman filter can eliminate measurement system error effectively. For the system of multi-sensor, standard algorithm does not work. To the problem, the paper presents multi-sensor centralized incremental Kalman filtering fusion algorithms, i.e., augmented fusion algorithm with incremental Kalman filter and sequential fusion algorithm with incremental Kalman filter. Based on the standard incremental Kalman filter, multi-sensor data fusion is implemented by using the ideas of augmented fusion and sequential fusion. Experimental results show that the fusion accuracy of the proposed centralized fusion algorithms is better than that of traditional centralized algorithms. Furthermore, the new fusion algorithms can eliminate the measurement system error.

Key words: multi-sensor data fusion, incremental Kalman filter, augmented fusion, sequential fusion, measurement system error