Modulo sampling has recently drawn a great deal of attention for cutting-edge applications, due to overcoming the barrier of information loss through sensor saturation and clipping. This is a significant problem, especially when the range of signal amplitudes is unknown or in the near-far case. To overcome this fundamental bottleneck, we propose a one-bit-aided (1bit-aided) modulo sampling scheme for direction-of-arrival (DOA) estimation. On the one hand, one-bit quantization involving a simple comparator offers the advantages of low-cost and low-complexity implementation. On the other hand, one-bit quantization provides an estimate of the normalized covariance matrix of the unquantized measurements via the arcsin law. The estimate of the normalized covariance matrix is used to implement blind integer-forcing (BIF) decoder to unwrap the modulo samples to construct the covariance matrix, and subspace methods can be used to perform the DOA estimation. Our approach named as 1bit-aided-BIF addresses the near-far problem well and overcomes the intrinsic low dynamic range of one-bit quantization. Numerical experiments validate the excellent performance of the proposed algorithm.

翻译：模数采样因其能够克服传感器饱和与裁剪导致的信息损失瓶颈，在尖端应用中近来备受关注。当信号幅度范围未知或存在远近效应时，这一问题尤为突出。为突破这一根本性瓶颈，我们提出一种面向到达方向（DOA）估计的单比特辅助（1bit-aided）模数采样方案。一方面，基于简单比较器的单比特量化具有低成本、低复杂度的实现优势；另一方面，单比特量化通过反正弦定律可提供未量化测量的归一化协方差矩阵估计。该归一化协方差矩阵估计用于实现盲整数强制（BIF）解码器以解包裹模数样本，从而构建协方差矩阵，进而利用子空间方法完成DOA估计。我们提出的名为1bit-aided-BIF的方法能有效解决远近问题，并克服单比特量化固有的低动态范围缺陷。数值实验验证了所提算法的卓越性能。