# One-Bit MUSIC

## TL;DR

This paper introduces a simple and effective method called one-bit MUSIC for estimating the direction-of-arrival using only one-bit quantized array measurements, achieving performance comparable to traditional methods.

## Contribution

It proposes a novel subspace-based DOA estimation technique that works directly with one-bit measurements without reconstructing unquantized covariance matrices.

## Key findings

- The method accurately estimates DOA with one-bit data.
- Performance is comparable to unquantized covariance matrix methods.
- The approach is easy to implement in practical systems.

## Abstract

In this letter, we consider the problem of direction-of-arrival (DOA) estimation with one-bit quantized array measurements. With analysis, it is shown that, under mild conditions the one-bit covariance matrix can be approximated by the sum of a scaled unquantized covariance matrix and a scaled identity matrix. Although the scaling parameters unknown because of the extreme quantization, they do not affect the subspace-based DOA estimators. Specifically, the signal and noise subspaces can be straightforwardly determined through the eigendecomposition of the one-bit covariance matrix, without pre-processing such as unquantized covariance matrix reconstruction. With so-obtained subspaces, the most classical multiple signal classification (MUSIC) technique can be applied to determine the signal DOAs. The resulting method is thus termed as one-bit MUSIC. Thanks to the simplicity of this method, it can be very easily implemented in practical applications, whereas the DOA estimation performance is comparable to the case with unquantized covariance matrix reconstruction, as demonstrated by various simulations.