ResNet Applied for a Single-Snapshot DOA Estimation

M.L. Lima de Oliveira, Marco J.G. Bekooij

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

9 Citations (Scopus)
140 Downloads (Pure)


In this paper, we discuss the usage of Residual Neural Networks (ResNets) for calculating the Direction Of Arrival (DOA) of MIMO radars and the estimation of the number of targets, using Minimum Redundancy Array (MRA). In addition, we are considering only the case of one snapshot at a time. This means that most techniques would deliver a poor estimation performance, whereas the Maximum Likelihood Estimation (MLE) algorithm delivers decent performance at a high computational cost. ResNet appears to be a viable alternative solution for this problem, being able to outperform MLE in some cases while having a less computational cost for scenarios with at least two different targets.
Original languageEnglish
Title of host publication2022 IEEE Radar Conference (RadarConf22)
ISBN (Electronic)978-1-7281-5368-1
Publication statusPublished - 3 May 2022
EventIEEE Radar Conference, RadarConf 2022 - New York, United States
Duration: 21 Mar 202225 Mar 2022


ConferenceIEEE Radar Conference, RadarConf 2022
Abbreviated titleRadarConf 2022
Country/TerritoryUnited States
CityNew York


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