杂波先验数据缺失条件下基于级联优化处理的雷达波形设计方法

认知雷达波形设计往往依赖于精准的杂波先验信息，当先验信息数据存在缺失时，所构建的杂波模型会严重失配，进而影响雷达对杂波的抑制能力。该文针对杂波先验数据缺失条件下的雷达波形优化问题，建立完全随机缺失机制下的点状与块状缺失场景，设计恒模与相似性约束的波形优化模型，提出基于优先级填充-强化学习级联优化的雷达波形训练算法:即采用强化学习智能体与填充算法修复后的杂波环境相交互的级联方法，以最大化信杂噪比为优化目标，通过迭代训练得到雷达最佳波形参数配置策略。最后，仿真验证不同缺失概率条件下所提算法的优越性。结果表明:相比于传统非级联优化算法，该文所提算法均可获得更优的杂波抑制性能，有效提升雷达的探测能力。 

Radar Waveform Design Method Based on Cascade Optimization Processing under Missing Clutter Prior Data

Cognitive radar waveform design often relies on accurate clutter prior information. When prior information data is missing, the constructed clutter model will be severely mismatched, affecting the radar’s ability to suppress clutter. Aiming at the radar waveform optimization problem under missing clutter prior data, this paper establishes point and block-like missing scenarios under the completely random missing mechanism, designs a waveform optimization model with constant modulus and similarity constraints, and proposes a radar waveform training algorithm based on priority filling?reinforcement learning cascade optimization: that is, a cascade method in which the reinforcement learning agent interacts with the clutter environment repaired by a filling algorithm, with the optimization goal of maximizing the signal-to-noise ratio, and the optimal configuration strategy with waveform parameters is obtained through iterative training. Finally, simulations verify the superiority of the proposed algorithm under different missing probability conditions. The results show that the proposed algorithm outperforms the traditional non-cascading optimization algorithm, regarding clutter suppression and effectively improves the detection ability of radar.