针对子阵级多输入多输出(MIMO)雷达阵列孔径数量有限带来的高方向图旁瓣问题,构建了子阵级MIMO 雷达收发信号和多子阵角度测量模型,提出了基于自适应迭代重加权(AIR)和p-范数约束下迭代重加权最小二乘(p-IRLS)的角度超分辨算法,分析了两种算法的计算复杂度。在仿真实验中验证了两种算法的性能,并对其在单目标和双目标、以及干扰环境检测中的效果进行了比较分析。仿真结果表明,AIR 和p-IRLS 算法能够有效降低子阵级MIMO 雷达的方向图旁瓣电平,同时实现对目标的角度超分辨。和AIR 算法相比,p-IRLS 算法在低信噪比和邻近目标分辨性能更突出。

A subarray-level MIMO radar transceiver signal and multi-subarray angular measurement model is constructed in this paper to address the problem of high directional map sidelobes caused by the limited number of apertures of subarray-level multipleinput multiple-output (MIMO) radar arrays. Angular super-resolution algorithms based on adaptive iterative reweighted (AIR) and iterative reweighted least squares with p-norm constraint (p-IRLS) are proposed and the computational complexity of the two algorithms is analyzed. The performance of the two algorithms is verified in simulation experiments, and their effects in single and dualtarget, and interference environment detection are comparatively analyzed. The simulation results show that the AIR and p-IRLS algorithms are able to effectively reduce the level of directional map sidelobes in subarray-level MIMO radars, while realizing angular super-resolution of targets. Compared with the AIR algorithm, the p-IRLS algorithm is more prominent in low signal-to-noise ratio and neighboring target resolution performance.

TN972