Kavli Affiliate: Wei Gao
| First 5 Authors: [#item_custom_name[1]], [#item_custom_name[2]], [#item_custom_name[3]], [#item_custom_name[4]], [#item_custom_name[5]]
| Summary:
To satisfy the high-resolution requirements of direction-of-arrival (DOA)
estimation, conventional deep neural network (DNN)-based methods using grid
idea need to significantly increase the number of output classifications and
also produce a huge high model complexity. To address this problem, a
multi-level tree-based DNN model (TDNN) is proposed as an alternative, where
each level takes small-scale multi-layer neural networks (MLNNs) as nodes to
divide the target angular interval into multiple sub-intervals, and each output
class is associated to a MLNN at the next level. Then the number of MLNNs is
gradually increasing from the first level to the last level, and so increasing
the depth of tree will dramatically raise the number of output classes to
improve the estimation accuracy. More importantly, this network is extended to
make a multi-emitter DOA estimation. Simulation results show that the proposed
TDNN performs much better than conventional DNN and root-MUSIC at extremely low
signal-to-noise ratio (SNR), and can achieve Cramer-Rao lower bound (CRLB).
Additionally, in the multi-emitter scenario, the proposed Q-TDNN has also made
a substantial performance enhancement over DNN and Root-MUSIC, and this gain
grows as the number of emitters increases.
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