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To satisfy the high-resolution requirements of direction-of-arrival (DOA)estimation, conventional deep neural network (DNN)-based methods using grididea need to significantly increase the number of output classifications andalso produce a huge high model complexity. To address this problem, amulti-level tree-based DNN model (TDNN) is proposed as an alternative, whereeach level takes small-scale multi-layer neural networks (MLNNs) as nodes todivide the target angular interval into multiple sub-intervals, and each outputclass is associated to a MLNN at the next level. Then the number of MLNNs isgradually increasing from the first level to the last level, and so increasingthe depth of tree will dramatically raise the number of output classes toimprove the estimation accuracy. More importantly, this network is extended tomake a multi-emitter DOA estimation. Simulation results show that the proposedTDNN performs much better than conventional DNN and root-MUSIC at extremely lowsignal-to-noise ratio (SNR), and can achieve Cramer-Rao lower bound (CRLB).Additionally, in the multi-emitter scenario, the proposed Q-TDNN has also madea substantial performance enhancement over DNN and Root-MUSIC, and this gaingrows as the number of emitters increases.

A Novel Tree Model-based DNN to Achieve a High-Resolution DOA Estimation via Massive MIMO receive array