Multi-sensor track fusion algorithm research and simulation analysis
Shouqing Chen, Huijing Dou, Zonghao Liu, Mao Yu Zheng · 2022 Global Conference on Robotics, Artificial Intelligence and Information Technology (GCRAIT) · 2022
Target tracking is extremely important in the military field. In multi-sensor data fusion processing, track fusion techniques are required to make the target position information more accurate and reliable. In this paper, the target motion uses uniform and uniformly accelerated motion models, and the smoothing of the motion trajectory uses Kalman filtering. In this paper, Convex combination fusion algorithm, covariance weighted fusion algorithm and adaptive fusion algorithm are used to fuse the track data, and analyze the performance of these three algorithms with fusion trajectory, root mean square error of fusion comparison and algorithm time complexity comparison. The simulation results show that all three algorithms can extract useful information from the track data and improve the target tracking accuracy.