In this paper, a sparse recovery algorithm based on a double-pulse FDA-MIMO radar is proposed to jointly extract the angle and range estimates of targets. Firstly, the angle estimates of targets are calculated by transmitting a pulse with a zero frequency increment and employing the improved -SVD method. Subsequently, the range estimates of targets are achieved by utilizing a pulse with a nonzero frequency increment. Specifically, after obtaining the angle estimates of targets, we perform dimensionality reduction processing on the overcomplete dictionary to achieve the automatically paired range and angle in range estimation. Grid partition will bring a heavy computational burden. Therefore, we adopt an iterative grid refinement method to alleviate the above limitation on parameter estimation and propose a new iteration criterion to improve the error between real parameters and their estimates to get a trade-off between the high-precision grid and the atomic correlation. Finally, the proposed algorithm is evaluated by providing the results of the Cramér-Rao lower bound (CRLB) and numerical root mean square error (RMSE).

中文翻译：

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通过双脉冲FDA-MIMO雷达进行目标距离角定位的精确稀疏恢复算法

提出了一种基于双脉冲FDA-MIMO雷达的稀疏恢复算法，以联合提取目标的角度和距离估计。首先，通过发射频率增量为零的脉冲并采用改进的-SVD方法。随后，通过利用具有非零频率增量的脉冲来实现目标的范围估计。具体而言，在获得目标的角度估计后，我们对超完备字典执行降维处理，以实现范围估计中自动配对的范围和角度。网格划分将带来沉重的计算负担。因此，我们采用迭代网格细化方法来缓解参数估计的上述局限性，并提出一种新的迭代准则，以改善实际参数及其估计之间的误差，从而在高精度网格和原子相关性之间进行权衡。最后，通过提供Cramér-Rao下界（CRLB）和数值均方根误差（RMSE）的结果对提出的算法进行评估。