Deep Learning for DOA Estimation in MIMO Radar Systems via Emulation of Large Antenna Arrays
This addresses improved direction-finding accuracy for radar systems, though it appears incremental as it builds on existing MUSIC methods.
The paper tackles DOA estimation in MIMO radar by using deep learning to emulate large antenna arrays from small ones, achieving better performance than MUSIC with actual large arrays in high-angle and low-SNR scenarios.
We present a MUSIC-based Direction of Arrival (DOA) estimation strategy using small antenna arrays, via employing deep learning for reconstructing the signals of a virtual large antenna array. Not only does the proposed strategy deliver significantly better performance than simply plugging the incoming signals into MUSIC, but surprisingly, the performance is also better than directly using an actual large antenna array with MUSIC for high angle ranges and low test SNR values. We further analyze the best choice for the training SNR as a function of the test SNR, and observe dramatic changes in the behavior of this function for different angle ranges.