Environment Sensing Information Aided Waveform Parameters Intelligent Selection for Cognitive Radar

Multi-function radar can perform various missions simultaneously in a complex and variable electromagnetic environment. How to improve the intelligence level of radars, so that they can adapt to the change of environment and the requirements of tasks, has gradually become a high-profile the research emphasis. Aiming at the optimization problem of maneuvering target detection and tracking performance in clutter environment, this paper proposes an intelligent scheduling algorithm for radar waveform parameters based on environmental perception. The reward function is designed under the maximum signal-to-noise ratio criterion and the minimum mean square error criterion. In this paper, And the Q learning and deep Q learning network are used for training. Through the interaction between the radar and the environment, the multi-frame clutter information is fully utilized which. It can effectively avoid the clutter occlusion problem caused by blur, and improve the SNR and tracking accuracy. The results of the airborne radar simulation experiments show that in the process of maneuvering target detection and tracking in clutter environment, the proposed environment -sensing and information -assisted waveform intelligent selection scheme proposed in this paper can achieve better higher processing efficiency and greater performance with improved processing efficiencyimprovement than the traditional heuristic algorithms.