Multi-sensor Online Track Fusion Algorithm Based on Multi-output
Multi-sensor track fusion can integrate data under certain criteria,and obtain state estimation values and more valuable comprehensive information that are closer to the true values of the measured physical quantities than a single sensor data.At pres-ent,although the Kalman filter algorithm and the traditional weighted average fusion algorithm can effectively realize track fusion,the measurement accuracy obtained by fusion is still limited.According to the characteristics of track data,a multi-sensor online track fusion algorithm based on multi-output is proposed in this paper,and the multi-output multiple linear regression model is used to realize the track data fusion.The cloud-edge collaborative architecture is used to complete the model training and inference,and the multi-output multiple linear regression model is trained by using historical data in the central cloud,moreover the online gradi-ent descent algorithm is used at the edge to complete the real-time update of the model weight coefficient on this basis.Experimen-tal results show that the proposed algorithm is better than the current Kalman filter algorithm and the traditional weighted average fusion algorithm in terms of fusion accuracy.
multi-outputmultiple linear regressiononline learningtrack fusion
万方数据
维普
