Fixed-Threshold One-Bit Toeplitz Covariance Estimation under Sparse-Ruler Sampling

Abstract

We estimate the Toeplitz covariance matrix of a centered Gaussian distribution from data that are both coarsely quantized and sparsely sampled. Only the coordinates of a sparse ruler are recorded, and each recorded value is kept as a single bit: the sign of its comparison with a fixed threshold. Such data arise in low-precision sensing front ends and sparse sensor arrays. Because the threshold is nonzero, every bit has a common mean. Each bit is also reused across many of the products that build the covariance, so one bit's error enters many of them. Centering removes the shared error. We prove a Gaussian variance contraction theorem for products of a centered, bounded nonlinearity of a Gaussian vector, the non-smooth one-bit sign included; it sets each lag's variance by how well the ruler covers that lag. The resulting estimator needs neither the signal scale nor the bit mean in advance, since the nonzero threshold makes both identifiable from the marginal bits. A matching minimax lower bound shows the resulting coverage rate is optimal up to constants over a neighborhood of white noise; the bound holds even for the unquantized real-valued samples, so one-bit quantization costs only a constant factor.