Fusion Model Using a Neural Network and MLE for a Single Snapshot DOA Estimation with Imperfection Mitigation

Marcio L.Lima De Oliveira*, Marco J.G. Bekooij

*Corresponding author for this work

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In this paper, we discuss the fusion of deep neural networks with the Maximum Likelihood Estimation (MLE) algorithm for estimating both the Direction Of Arrival (DOA) of MIMO radars and the number of sources for a single-snapshot scenario using a Minimum Redundancy antenna Array (MRA) with imperfections and evaluating it with synthetic and real-world data. The combination of Deep Learning (DL) with the classical MLE seems to be a viable solution for this problem, as it is less computationally intensive than a classical MLE while not losing generalization and being better at estimating the number of sources. In both our experiments, using synthetic and real-world data, our method performs close to MLE and appears to be a deployable solution for real scenarios. Besides reducing the computational load of MLE, the novelty of our model lies in the fact that the deep learning model learns the gain and phase errors as a function of the direction of arrival instead of applying a simple calibration.

Original languageEnglish
Title of host publication2023 IEEE International Radar Conference, RADAR 2023
ISBN (Electronic)9781665482783
Publication statusPublished - 28 Dec 2023
Event2023 International Radar Conference, RADAR 2023 - Sydney, Australia
Duration: 5 Nov 202310 Nov 2023

Publication series

NameProceedings of the IEEE Radar Conference
ISSN (Print)1097-5764
ISSN (Electronic)2375-5318


Conference2023 International Radar Conference, RADAR 2023
Abbreviated titleRADAR 2023
Internet address


  • 2024 OA procedure
  • Deep Learning
  • Direction of Arrival
  • Machine Learning
  • Maximum Likelihood Estimation
  • Antenna Imperfection Mitigation


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