【摘要】 针对强杂波背景下的捷变频回波信号目标检测问题，文中提出了一种基于智能认知的稀疏重构算法，获取箔条杂波环境下的目标与杂波散射点的距离与速度估计。该方法将箔条杂波的速度范围与目标所在粗分辨距离门信息作为先验知识，在迭代过程中率先检测出强杂波散射点，实现杂波与目标散射点的分离，完成目标的检测与参数估计。此外，针对稀疏度对算法重构性能的影响，文中提出了根据杂波与目标散射点个数自适应选取合适的捷变频脉冲数进行稀疏场景重构的方法，能够在完整重构雷达观测场景中散射点的同时降低算法的计算量，提升目标检测的效率。更多还原

【Abstract】 This paper proposes a sparse reconstruction algorithm based on intelligent cognition for target detection in frequency agile echo signals under strong clutter background, which estimates the distance and velocity between targets and clutter scattering points in chaff clutter environment. This method takes the velocity range of chaff clutter and the coarse resolution range gate information of the target as prior knowledge, and first detects strong clutter scattering points during the iteration process, achieving the separation of clutter and target scattering points, and completing target detection and parameter estimation. In addition, in response to the impact of sparsity on algorithm reconstruction performance, this paper proposes a method of adaptively selecting an appropriate number of agile frequency conversion pulses based on the number of scattered points between clutter and target for sparse scene reconstruction. This method can reduce the computational complexity of the algorithm and improve the efficiency of target detection while fully reconstructing the scattered points in the radar observation scene.更多还原