Abstract
Recently one-bit quantization has been widely used in field of signal processing because of its advantages of low cost, low power consumption and high sampling rate. This paper studies the problem of estimating the direction of arrivals (DOAs) of source signals via sparse linear arrays (SLAs) using one-bit measurements. By constructing an augmented covariance matrix from the sample covariance matrix, we can realize gridless DOA estimation using root-MUSIC on the augmented covariance matrix and solve more sources than the number of sensors. Two covariance fitting criteria, including maximum likelihood and Wasserstein distance, are introduced into one-bit sparse array DOA estimation. Numerical simulations demonstrate that Wasserstein distance based criterion has good performance both in accuracy and efficiency.
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