基于时-空稀疏解的方位超分辨算法

Azimuth Super-Resolution Algorithm Based on Temporal-Spatial Sparse Solution

受限于阵列波束宽度的瑞利限,常规单脉冲测角方法无法分辨处于一个波束宽度内的多个目标,为此,提出一种基于时-空稀疏解的方位超分辨算法,对距离、多普勒维无法分辨的同一波束宽度内的多个目标进行角度测量。该方法首先构造基于类-p范数稀疏度和2范数约束的代价函数,通过迭代过程最小化代价函数求得时-空二维稀疏解,最后由时-空二维稀疏解估计目标的多普勒频率和方位角。与传统的方位超分辨算法相比,该方法利用了多普勒信息,改善了方位超分辨性能,具有较高的分辨率和极低的旁瓣电平,同时该方法无需多维空间谱搜索过程。计算机仿真结果验证了该算法的有效性。

Traditional angle estimation methods,such as mono-pulse,cant resolve multiple targets within a beam due to Rayleigh Bound.An azimuth super-resolution method based on temporal-spatial sparse solution is proposed to resolve and estimate the azimuth angles of multiple targets which cant be resolved by range and Doppler frequency.The algorithm starts with the cost function based on a p-norm-like（l（p≤1））di-versity measure with a 2-norm constraint.The sparse solution is then iteratively obtained by minimizing the cost function.Finally,the Doppler frequencies and azimuth angles of targets can be estimated using the tem-poral-spatial sparse solution.By using the Doppler information,the proposed method achieves higher resolu-tion compared to the traditional high-resolution methods but without multi-dimensional spectral search.Sim-ulations results are presented to verify the effectiveness of the method.