Deep-MLE: Fusion between a Neural Network and MLE for a Single Snapshot DOA Estimation

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Abstract

In this paper, we propose a novel framework called DeepMLE, which gives a solution to the single-snapshot Direction Of Arrival (DOA) estimation problem, up to 4 distinct targets, using a radar equipped with a Minimum Redundancy antenna Array (MRA). This framework works by fusing a Deep Learning (DL) technique - 1D Residual Neural Network (1D ResNet) - with a classical DOA algorithm - Maximum Likelihood Estimation (MLE). By combining two very different approaches, we can address some of their limitations, such as the computational complexity of MLE. On the other hand, our proposed Deep-MLE uses MLE to correct, to some degree, the estimations made by the Neural Network (NN). The results from our framework are promising as it seems to be a viable solution to the DOA estimation problem, having a better performance than models using pure MLE or NN.
Original languageEnglish
Title of host publication ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Pages3673-3677
Number of pages5
ISBN (Electronic)978-1-6654-0540-9
DOIs
Publication statusPublished - 22 Apr 2022
EventIEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 2022 - Singapore, Singapore
Duration: 23 May 202227 May 2022

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 2022
Abbreviated titleICASSP 2022
Country/TerritorySingapore
CitySingapore
Period23/05/2227/05/22

Keywords

  • 22/2 OA procedure

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