Compressive sensing has attracted considerable attention in automotive radar interference mitigation. However, these algorithms usually cannot be applied directly to commercial automotive radar as most of them are computationally intense. In this paper, we therefore introduce a computationally efficient two-dimensional masked residual updates (2D MRU) compressive sensing framework. By utilizing the sparsity of the beat signal in the frequency domain, the range-Doppler (RD) spectrum can be reconstructed with the help of undistorted samples in the beat signal. Unlike the other schemes, where a 2D signal measurement is vectorized into a 1D signal, the proposed 2D MRU can directly take a 2D signal measurement and reconstruct the corresponding RD spectrum. Furthermore, the 2D MRU framework can be easily integrated into well-known optimization schemes such as basis pursuit, iterative hard thresholding, iterative soft thresholding, orthogonal matching pursuit, and approximate message-passing algorithm. In addition to the standard iterative thresholding algorithms, we propose a novel prior-model-based iterative thresholding method to further reduce the computation time and reconstruction error. Theoretical analysis shows that the proposed framework can successfully reconstruct the RD spectrum with high probability. Moreover, numerical experiments demonstrate the superiority of the proposed framework in terms of computational complexity.

压缩传感在汽车雷达干扰缓解方面引起了相当大的关注。然而，这些算法通常不能直接应用于商用汽车雷达，因为它们中的大多数都是计算密集型的。因此，在本文中，我们介绍了一种计算效率高的二维掩码残差更新 (2D MRU) 压缩感知框架。通过利用频域中差拍信号的稀疏性，可以借助差拍信号中的未失真样本重建距离多普勒 (RD) 频谱。与将 2D 信号测量矢量化为 1D 信号的其他方案不同，所提出的 2D MRU 可以直接进行 2D 信号测量并重建相应的 RD 频谱。此外，2D MRU 框架可以很容易地集成到众所周知的优化方案中，例如基追踪、迭代硬阈值、迭代软阈值、正交匹配追踪和近似消息传递算法。除了标准的迭代阈值算法外，我们还提出了一种新颖的基于先验模型的迭代阈值方法，以进一步减少计算时间和重建误差。理论分析表明，所提出的框架能够以高概率成功地重建 RD 频谱。此外，数值实验证明了所提出的框架在计算复杂性方面的优越性。除了标准的迭代阈值算法外，我们还提出了一种新颖的基于先验模型的迭代阈值方法，以进一步减少计算时间和重建误差。理论分析表明，所提出的框架能够以高概率成功地重建 RD 频谱。此外，数值实验证明了所提出的框架在计算复杂性方面的优越性。除了标准的迭代阈值算法外，我们还提出了一种新颖的基于先验模型的迭代阈值方法，以进一步减少计算时间和重建误差。理论分析表明，所提出的框架能够以高概率成功地重建 RD 频谱。此外，数值实验证明了所提出的框架在计算复杂性方面的优越性。