An intelligent radar predictor for high-speed moving-target tracking

Due to rapid the increase in missile speed, the air-defense radar system faces severe challenge in tracking these high-speed missiles. During tracking, the radar data are read into the system in a real-time manner sequentially, and thus only few data are available for trajectory estimation in every short time period. Therefore, in this paper, we propose an intelligent radar predictor, including a self-organizing map (SOM), to achieve accurate trajectory estimation under the strict time constraint. By knowing the dynamic model of the moving target, the SOM, an unsupervised neural network, learns to predict the target trajectory using a limited number of data. The performance of the SOM is compared with that of the Kalman filtering. Simulation results based on both the generated and real radar data demonstrate the effectiveness of the proposed intelligent radar predictor.