1 research outputs found
Deep Learning-Aided Subspace-Based DOA Recovery for Sparse 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.Comment: 4 pages, 4 figure