  Direction of arrival (DoA) estimation is a fundamental task in array
processing. A popular family of DoA estimation algorithms are subspace methods,
which operate by dividing the measurements into distinct signal and noise
subspaces. Subspace methods, such as Multiple Signal Classification (MUSIC) and
Root-MUSIC, rely on several restrictive assumptions, including narrowband
non-coherent sources and fully calibrated arrays, and their performance is
considerably degraded when these do not hold. In this work we propose
SubspaceNet; a data-driven DoA estimator which learns how to divide the
observations into distinguishable subspaces. This is achieved by utilizing a
dedicated deep neural network to learn the empirical autocorrelation of the
input, by training it as part of the Root-MUSIC method, leveraging the inherent
differentiability of this specific DoA estimator, while removing the need to
provide a ground-truth decomposable autocorrelation matrix. Once trained, the
resulting SubspaceNet serves as a universal surrogate covariance estimator that
can be applied in combination with any subspace-based DoA estimation method,
allowing its successful application in challenging setups. SubspaceNet is shown
to enable various DoA estimation algorithms to cope with coherent sources,
wideband signals, low SNR, array mismatches, and limited snapshots, while
preserving the interpretability and the suitability of classic subspace
methods.
