Modulo sampling or unlimited 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 compared to using a high-precision ADC directly in the given set up.

翻译：模数采样（或无限制采样）近期在尖端应用中备受关注，因其克服了传感器饱和与削波导致的信息损失瓶颈。这一难题在信号幅度范围未知或远近效应场景中尤为突出。为突破这一根本性障碍，本文提出一种单比特辅助（1bit-aided）模数采样方案，用于波达方向（DOA）估计。一方面，基于简单比较器的单比特量化具有低成本和低复杂度实现的优势；另一方面，单比特量化通过反正弦定律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值用于实现盲整数强制（BIF）解码器，以解包裹模数样本构建协方差矩阵，进而采用子空间方法进行DOA估计。所提方法命名为1bit-aided-BIF，有效解决了远近效应问题，并克服了单比特量化固有的低动态范围缺陷。数值实验验证了该算法在给定场景下相较直接使用高精度ADC展现出的优越性能。