A Data Fusion Method of Radar Telemetry Integrated System Based on Kalman Filter

In the traditional range measurement and control system, radar and telemetry systems are independently constructed. There are problems such as equipment redundancy, insufficient utilization of multi-source tracking data, and insufficient connection between tracking and data processing. Therefore, this paper proposes a radar telemetry integration system architecture and an adaptive weighted data fusion method, which makes full use of the response, reflection and telemetry tracking data of the radar telemetry integration system. The advantages of comprehensiveness realize the optimal fusion and closed-loop tracking control of multi-source heterogeneous data. Firstly, the local filter is designed according to the characteristics of the three types of data. Then, based on the minimum mean square error criterion, the adaptive weighting factor is designed based on the local filter estimation covariance to complete the global optimal track fusion. Finally, the fusion track results are fed back to the tracking scheduling module to drive the servo system to achieve high-precision closed-loop follow-up tracking. The simulation results show that the tracking accuracy of this method is more than 15 % higher than that of a single sensor in the conventional scene, and it can still maintain stable tracking performance in abnormal scenes such as target echo fluctuation, accidental interference and data packet loss. It effectively solves the adaptive problem of multi-source data fusion and the linkage optimization problem of tracking closed-loop in radar telemetry integrated system, and has strong engineering application value.