低空条件下认知MIMO雷达目标跟踪中的资源分配技术研究

针对低空条件下MIMO雷达目标跟踪过程中难以适应动态杂波、多径影响，导致物理资源利用率低下，性能受限的问题，利用认知理论中的闭环反馈机制实时获取目标、环境变化，优化资源分配。通过设计贝叶斯检测器，利用目标先验信息实时调整检测门限，以适应杂波剧变；采用稀疏化回波建模，将多路径状态与目标状态共同递推估计，以利用多径信号能量提升信噪比，跟踪多径影响；从多样性杂波和多径效应两项干扰因素着手，试图得出低空条件下目标跟踪的后验克拉美罗界，为资源分配提供优化准则；根据优化模型特点，采用凸松弛、凸优化、循环最小化等算法进行高效求解，在满足雷达实时性要求的同时，实现多种资源的联合优化分配。项目研究成果可为低空传感器阵列资源管理提供理论依据和技术支持，对赢得未来防空战场主动权具有重要意义。

Due to the effect of dynamic clutter and multipath, the low efficiency of physical resource utilization restricts the high performance of MIMO radar in tracking low-altitude targets. The closed loop feedback scheme implied in the cognition theory is utilized to acquire the change of dynamic target and environment and to optimize the resource allocation. The Bayesian detector, where the target prior information is exploited to adapt the threshold to the clutter upheaval, is designed. The multipath state and the target state are estimated simultaneously by the sparse signal modeling. Thereby, the energy of multipath is utilized to improve the signal-to-noise ratio, and the multipath effect is tracked. Considering the effect of variable clutter and multipath, the posterior Cramér–Rao lower bound (PCRLB) for target tracking under low-altitude background is tried to be derived and then employed to serve as the optimization criterion. On the basis of characteristics of optimization model, the convex relaxation, convex optimization and cyclical minimizer algorithm are utilized as the efficient solver to realize the joint of multiple resource allocation. The research results can provide the theoretical and technical support for sensor array resource management in low-altitude, and provide a promising way to regain the initiative in the future air-defense wars.