Super-resolution DOA estimation for correlated off-grid signals via deep estimator

This paper develops a deep estimator framework of deep convolution networks (DCNs) for super-resolution direction of arrival (DOA) estimation. In addition to the scenario of correlated signals, the quantization errors of the DCN are the major challenge. In our deep estimator framework, one DCN is used for spectrum estimation with quantization errors, and the remaining two DCNs are used to estimate quantization errors. We propose training our estimator using the spatial sampled covariance matrix directly as our deep estimator's input without any feature extraction operation. Then, we reconstruct the original spatial spectrum from the spectrum estimate and quantization errors estimate. Also, the feasibility of the proposed deep estimator is analyzed in detail in this paper. Once the deep estimator is appropriately trained, it can recover the correlated signals' spatial spectrum fast and accurately. Simulation results show that our estimator performs well in both resolution and estimation error compared with the state-of-the-art algorithms.