This letter studies the problem of Direction of Arrival (DoA) estimation from low-resolution few-bit quantized data collected by Sparse Linear Array (SLA). In such cases, contrary to the one-bit quantization case, the well known arcsine law cannot be employed to estimate the covaraince matrix of unquantized array data. Instead, we develop a novel optimization-based framework for retrieving the covaraince matrix of unquantized array data from low-resolution few-bit measurements. The MUSIC algorithm is then applied to an augmented version of the recovered covariance matrix to find the source DoAs. The simulation results show that increasing the sampling resolution to 2 or 4 bits per samples could significantly increase the DoA estimation performance compared to the one-bit sampling regime while the power consumption and implementation costs is still much lower in comparison to the high-resolution sampling implementations.

中文翻译：

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使用低分辨率多位稀疏阵列测量进行 DoA 估计


这封信研究了从稀疏线性阵列 (SLA) 收集的低分辨率几位量化数据估计到达方向 (DoA) 的问题。在这种情况下，与一位量化情况相反，不能采用众所周知的反正弦定律来估计未量化阵列数据的协方差矩阵。相反，我们开发了一种新颖的基于优化的框架，用于从低分辨率的几位测量中检索未量化阵列数据的协方差矩阵。然后，将 MUSIC 算法应用于恢复的协方差矩阵的增强版本，以找到源 DoA。仿真结果表明，与一位采样方案相比，将采样分辨率提高到每个样本 2 或 4 位可以显着提高 DoA 估计性能，而与高分辨率采样相比，功耗和实现成本仍然低得多实施。