认知干扰功率优化分配策略研究

Recently, there is an increasing need of adaptivity and intelligence in the field of electronic warfare, resulting in the emergence of cognitive electronic warfare. Traditional electronic warfare suffers from the problems of low adaptive jamming signal, poor flexibility and insufficient usage of the target and environment information in the complex electromagnetic environment. To solve these problems, cognitive jamming power allocation strategy optimization methods which fully exploit the environment will be studied in this project. Jamming power allocation strategies are optimized based on the properly designed utility function, together with the information of radar waveform, power allocation method, working task of radar, the characteristics of target and environment and so on. Therefore, the jamming power allocation strategies will be more adaptive and the jamming performance will be improved. Further studies of jamming power allocation strategy will be based game theory. Non-cooperative game will be used to model the interaction between cognitive radar and a smart target equipped with a cognitive jammer. Jamming power allocation strategy will be obtained by playing with the power game between the jammer and radar. Results in this project will provide effective method of cognitive jamming power allocation strategy, which improves the jamming performance in complex battle field. Meanwhile, the methods are importance research in the realization of cognitive electronic warfare.

近年来，电子战领域对自适应、智能化要求越来越高，为认知电子战提供了旺盛的军事需求。为了解决传统干扰自适应程度低、灵活性能差、没有充分利用复杂电磁环境中的目标、环境等先验信息的问题，项目拟开展充分利用先验信息的干扰功率优化分配策略研究。通过充分利用对抗雷达的发射波形、功率分配和工作任务特点，选取合适的优化准则函数，设计考虑目标和环境先验信息的最优干扰功率优化分配策略，从而提升干扰自适应程度和干扰性能。进一步的，考虑认知雷达和携带认知干扰机的智能目标对抗环境下的干扰功率分配策略，基于非合作博弈理论研究雷达和干扰在功率分配策略方面的博弈问题。项目研究不仅提供了有效的认知干扰功率分配策略，提高了复杂作战环境中的干扰效果，而且为认知电子战研究提供了重要方法。

为了提升未来智能对抗环境中的雷达干扰效果，项目以认知干扰机功率优化分配策略为研究目标，开展充分利用先验信息的认知干扰机单边功率优化分配研究、认知干扰机和认知雷达分层博弈模型下的功率优化分配策略研究和对称博弈模型下的功率优化分配策略研究三部分研究内容。通过分析干扰机对抗雷达功能用途，以雷达任务目的为牵引，梳理和总结了干扰机功率优化分配的准则函数，构建了基于CRB准则和基于MMSE准则的认知干扰机功率分配最优化问题，分别得到了干扰MIMO雷达运动参数估计性能和干扰MIMO雷达目标特性估计性能的最优干扰功率分配策略。进一步的，考虑到对抗雷达的智能化水平及可能采用的雷达波形优化抗干扰策略，项目基于MMSE准则函数，分别考虑了认知MIMO雷达和认知干扰机分层博弈模型下，Stackelberg博弈均衡时的最优干扰功率分配策略；以及认知雷达和认知干扰机对称博弈模型下，Nash均衡时的最优干扰功率分配策略。通过项目研究，提升了干扰机对环境先验信息的利用，并结合对抗雷达的智能优化算法和对抗场景，提出了相应的干扰策略，有效提升了干扰机在复杂目标环境下以及智能对抗环境下的干扰效果，为认知电子战智能决策算法提供了理论依据和算法支撑。