Sensor Systems for an Improved Track Fusion Algorithm to Optimize the Design of Target Precision Tracking

摘要

This study aims to optimize track association without a sensor system. Basing on a simple convex combination track fusion algorithm and the Bar Shalom-Campo track fusion algorithm, we designed an optimal distributed track fusion algorithm without feedback. This algorithm can be used to identify the computation cost without calculating the covariance and to reduce the difficulty of the operation with prior information. Simulation results show that the error of the fusion algorithm is smaller than of that of a single sensor. In addition, the target tracking accuracy of the track fusion algorithm is obviously higher than that of a single radar IMM filter, and this indicates that the system performance is significantly optimized. Implementing the multiple sensor fusion system can improve the accuracy of tracking targets. This study bears theoretical and practical significance in the analysis of the working capacity of a maximum optimization system.