Weighted Fusion of Heterogeneous Multi-Sensor Data Based on Gaussian Filtering and Mean Clustering
There are differences in the operating frequencies between heterogeneous multi-source sensors,resulting in poor consistency be-tween data and large observation error after weighted fusion. Therefore,a weighted fusion method based on Gaussian filtering and mean clustering is proposed for heterogeneous multi-source sensor data. Gaussian filtering is used to divide the data space cells of heterogeneous multi-source sensors,establish the best connected region based on cells,retain the internal data of sensors,and complete the Gaussian filte-ring smoothing of sensor data. Mean clustering is introduced to deal with the data consistency of heterogeneous multi-source sensors. The optimal weights and parameters are searched by immune particle swarm optimization, and the heterogeneous multi-source sensor data weighted fusion is completed by using the optimal weights and parameters. The simulation results show that the method can reduce the ob-servation error and mean square error of the fused sensor data,and the minimum values of the observation error and mean square error are both 0.002. Therefore,the availability of heterogeneous multi-source sensor data after fusion is improved.
heterogeneous multi-source sensorweighted data fusiongaussian filteringmean clustering
万方数据
维普
