A multi-sensor data fusion algorithm based on consistency preprocessing and adaptive weighting

In the data collection of a multi-sensor system, there are problems with large errors, conflicts, and redundancy. To solve the above problem, a multi-sensor data fusion algorithm based on anomaly data preprocessing and adaptive weighted estimation is proposed. To improve the reliability of the algorithm, first, for a single sensor measurement signal sequence, a consistency preprocessing using the off-centre distance method is performed, and the weighting factor of each measurement data is calculated. Then, the measurement signal sequence is weighted and fused; Secondly, in response to the uneven distribution of measurement errors among multiple sensors in different directions, an adaptive weighted data fusion method based on the principle of optimal weight allocation is proposed. The proposed method was compared with the adaptive weighting method and arithmetic mean method. The simulation results showed that the total mean square error of the data fusion results obtained using the proposed algorithm is smaller. The proposed algorithm can effectively improve the accuracy of data measurement, reduce redundancy, and improve the stability of data measurement.