认知雷达中基于Q学习的自适应波形选择算法

Adaptive waveform selection algorithm based on Q-learning in cognitive radar

自适应波形选择器是认知雷达中智能发射器的重要组成部分。有效的波形选择能够在不同的环境下选择发射最优的波形序列,从而以更高的精度追踪目标。针对雷达目标转移概率未知这一特点,把自适应波形选择问题建模为随机动态规划模型,提出应用Q学习的方法来解决这个问题。仿真结果说明,该算法接近于最优波形选择方案,并且状态估计的不确定性低于固定波形。

The adaptive waveform selector is an important part of intelligent transmitters in cognitive radar. Effective waveform selection can transmit an optimal waveform sequence in different environments so as to track targets with higher accuracy.The problem of adaptive waveform selection is modeled as a stochastic dynamic model, and a Q-learning method is proposed to solve this problem under the fact that the transition probabilities of radar targets are unknown.The simulation results demonstrate that the proposed algorithm approaches the optimal waveform selection scheme and has a lower uncertainty of state estimation compared with the fixed waveform.