Sparse Bayesian Learning for Direct Position Determination with Mutual Coupling

Fei Ma, Yuexian Wang, Ling Wang, Chuang Han

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

2 Scopus citations

Abstract

Based on sparse Bayesian learning, a novel direct position determination (DPD) algorithms with mutual coupling is presented. To reduce the computational complexity and correlation between nearby grid points, we utilize coarse non-uniformly sampled 2D grid. To handle the modeling error, we have theoretically derived the estimation expressions for the mutual coupling vectors. Simulation results show the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2021 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC-URSI 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages116-117
Number of pages2
ISBN (Electronic)9781946815101
DOIs
StatePublished - 2021
Event2021 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC-URSI 2021 - Singapore, Singapore
Duration: 4 Dec 202110 Dec 2021

Publication series

Name2021 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC-URSI 2021 - Proceedings

Conference

Conference2021 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC-URSI 2021
Country/TerritorySingapore
CitySingapore
Period4/12/2110/12/21

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