Deep Learning-Aided Subspace-Based DOA Recovery for Sparse Arrays
This addresses the challenge of DoA estimation in sparse arrays for applications like radar and wireless communications, offering an incremental improvement by integrating deep learning with model-based methods.
The paper tackles the problem of direction of arrival (DoA) recovery from sparse arrays with coherent sources and miscalibration by proposing Sparse-SubspaceNet, a deep learning method that learns to compute a surrogate virtual array covariance for subspace-based estimators, enabling accurate DoA estimation without requiring non-coherent signals or calibrated arrays.
Sparse arrays enable resolving more direction of arrivals (DoAs) than antenna elements using non-uniform arrays. This is typically achieved by reconstructing the covariance of a virtual large uniform linear array (ULA), which is then processed by subspace DoA estimators. However, these method assume that the signals are non-coherent and the array is calibrated; the latter often challenging to achieve in sparse arrays, where one cannot access the virtual array elements. In this work, we propose Sparse-SubspaceNet, which leverages deep learning to enable subspace-based DoA recovery from sparse miscallibrated arrays with coherent sources. Sparse- SubspaceNet utilizes a dedicated deep network to learn from data how to compute a surrogate virtual array covariance that is divisible into distinguishable subspaces. By doing so, we learn to cope with coherent sources and miscalibrated sparse arrays, while preserving the interpretability and the suitability of model-based subspace DoA estimators.