基于分层博弈的认知MIMO雷达资源管理方法研究

MIMO (multiple input multiple output) radar has a high degree of freedom in the aspects of spatial energy allocation and time resource scheduling, which can provide a more flexible resource allocation strategy for the target searching and tracking tasks. How to manage the MIMO radar resources optimally is an important way to improve the system probing performance, and it is also a key problem in the field of modern air defense warning detection. In the present methods of MIMO radar resource management, the overall efficiency of the system resources has not been taken into account, and the prior knowledge has not been utilized effectively, which have hindered the full play of the system detection performance. We will perform a research on MIMO radar resource management by the method of hierarchical game based on the closed-loop detection idea of cognitive radar. In this project, the relationship between the system resource allocation and the system probing performance will be analyzed based on the Bayesian detecting and tracking framework, and the inherent mechanism of cognitive radar resource management will be explored by sensing the target characteristics accurately. The resource allocation models will be established in the ubiquitous searching mode and simultaneous multi-beam tracking mode respectively, and the fast and robust optimization algorithms for solving the models will be studied. Taking the advantages of the cognitive detecting technology and the high degree of freedom in MIMO radar system, the project is expected to improve the overall utilized efficiency of the MIMO radar resources, and to improve the target detecting probability and tracking accuracy. This project can support the development of cognitive MIMO radar system in the basic theory and key technology.

MIMO雷达在空间能量分配和处理时间调度方面具有丰富的自由度，可以为目标搜索和跟踪任务提供多样灵活的资源配置方式。优化管理MIMO雷达系统资源是深入挖掘其探测潜能的关键途径，也是现代防空预警探测领域亟待解决的重要课题。目前MIMO雷达资源管理没有考虑系统资源的整体效能，缺乏对先验知识的有效利用，妨碍了系统探测性能的充分发挥。本项目拟结合认知雷达闭环探测思想，采用分层博弈的方法开展MIMO雷达资源管理的研究。基于贝叶斯检测跟踪框架，分析资源配置与目标检测跟踪性能的关系，通过对目标特性的准确感知，探明认知雷达资源管理的内在机理，分别针对泛探搜索和同时多波束跟踪模式建立系统资源的分层博弈分配模型，研究快速稳健的优化求解方法。本项目研究可以发挥认知探测技术和MIMO雷达系统自由度的优势，改善资源的整体利用效率，提高目标检测概率和跟踪精度，为认知MIMO雷达探测系统的发展提供基础理论和关键技术支撑。

MIMO雷达在空间能量分配和处理时间调度方面具有丰富的自由度，优化管理雷达发射端系统资源是深入挖掘其探测潜能的关键途径，也是现代防空预警探测领域亟待解决的重要课题。本项目结合认知雷达闭环探测思想，围绕雷达认知发射和接收端的自适应数据处理展开研究，主要工作包括：1）建立了针对目标跟踪的发射波形库，确定了发射波形选择准则，提出了针对目标跟踪的认知雷达发射波形选择方法；2）分别基于非合作博弈和合作博弈理论建立了MIMO雷达网络的功率分配模型，提出了面向目标检测和目标跟踪的分布式功率分配方法；3）面向复杂场景下的目标状态感知问题，提出了自适应机动目标跟踪算法和多维信息辅助的目标跟踪算法。本项目研究充分发挥认知探测技术和雷达发射端自由度的优势，改善系统资源的整体利用效率，提高目标检测概率和跟踪精度，为认知MIMO雷达探测系统的发展提供基础理论和关键技术支撑。