Deep Learning Based Computationally Efficient Unrolling IAA for Direction-of-Arrival Estimation

We introduce a computationally efficient approach for direction-of-arrival (DOA) estimation in automotive radar systems using a single-snapshot. Classical subspace-based methods like MUSIC and ESPRIT may apply spatial smoothing on uniform linear array to create multiple snapshots for accurate DOA estimation. However, spatial smoothing has the drawback of reducing the array aperture and it is not feasible for sparse linear arrays. The existing single-snapshot-based methods like compressive sensing and iterative adaptation approach (IAA) have high computational costs and slow convergence times, which poses challenges for real-time implementations. While strides in optimization algorithms and hardware acceleration strategies propose plausible remedies to alleviate these constraints, enhancing their appropriateness for real-time use, the computational cost remains notably high. The recent deep learning-based DOA estimation methods have shown good performance in terms of inference time and estimation accuracy, but lack interpretability and generalization. To address these limitations, we propose an unrolling iterative adaptive approach (UAA) that unrolls the IAA algorithm into multiple deep neural network layers. The UAA network has better generalization and avoids the high computational costs associated with matrix inversions. Extensive numerical experiments show that the UAA network outperforms IAA in terms of inference time and estimation accuracy under different signal-to-noise ratio (SNR) scenarios.