  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.
