Vector Approximate Message Passing for 3D MIMO Radar

Bastian Eisele; Ali Bereyhi; Ralf Müller; Sundeep Rangan

doi:10.1109/IEEECONF60004.2024.10943023

Vector Approximate Message Passing for 3D MIMO Radar

Bastian Eisele, Ali Bereyhi, Ralf Müller, Sundeep Rangan

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Compressive sensing has been widely used in MIMO radar to reduce the number of measurement antennas while preserving high spatial resolution. In this paper, we deal with the reconstruction of a 3D target scene in the array near-field. By modeling the depth dimension of the target as a property of the reflection coefficients, we can transform the problem of 3D target recovery into an equivalent 2D recovery task. In turn, we have to convert the measurement model to a distributed compressive sensing model to preserve the linearity of the measurement model. For this alternative problem, we develop a vector approximate message passing algorithm. We validate our proposed algorithm through numerical investigations.

Original language	English (US)
Title of host publication	Conference Record of the 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	1510-1516
Number of pages	7
ISBN (Electronic)	9798350354058
DOIs	https://doi.org/10.1109/IEEECONF60004.2024.10943023
State	Published - 2024
Event	58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024 - Hybrid, Pacific Grove, United States Duration: Oct 27 2024 → Oct 30 2024

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Conference

Conference	58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024
Country/Territory	United States
City	Hybrid, Pacific Grove
Period	10/27/24 → 10/30/24

Keywords

Approximate message passing
Compressive sensing
MIMO radar

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/IEEECONF60004.2024.10943023

Cite this

Eisele, B., Bereyhi, A., Müller, R., & Rangan, S. (2024). Vector Approximate Message Passing for 3D MIMO Radar. In M. B. Matthews (Ed.), Conference Record of the 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024 (pp. 1510-1516). (Conference Record - Asilomar Conference on Signals, Systems and Computers). IEEE Computer Society. https://doi.org/10.1109/IEEECONF60004.2024.10943023

Vector Approximate Message Passing for 3D MIMO Radar. / Eisele, Bastian; Bereyhi, Ali; Müller, Ralf et al.
Conference Record of the 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024. ed. / Michael B. Matthews. IEEE Computer Society, 2024. p. 1510-1516 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Eisele, B, Bereyhi, A, Müller, R & Rangan, S 2024, Vector Approximate Message Passing for 3D MIMO Radar. in MB Matthews (ed.), Conference Record of the 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024. Conference Record - Asilomar Conference on Signals, Systems and Computers, IEEE Computer Society, pp. 1510-1516, 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024, Hybrid, Pacific Grove, United States, 10/27/24. https://doi.org/10.1109/IEEECONF60004.2024.10943023

Eisele B, Bereyhi A, Müller R, Rangan S. Vector Approximate Message Passing for 3D MIMO Radar. In Matthews MB, editor, Conference Record of the 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024. IEEE Computer Society. 2024. p. 1510-1516. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/IEEECONF60004.2024.10943023

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abstract = "Compressive sensing has been widely used in MIMO radar to reduce the number of measurement antennas while preserving high spatial resolution. In this paper, we deal with the reconstruction of a 3D target scene in the array near-field. By modeling the depth dimension of the target as a property of the reflection coefficients, we can transform the problem of 3D target recovery into an equivalent 2D recovery task. In turn, we have to convert the measurement model to a distributed compressive sensing model to preserve the linearity of the measurement model. For this alternative problem, we develop a vector approximate message passing algorithm. We validate our proposed algorithm through numerical investigations.",

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AB - Compressive sensing has been widely used in MIMO radar to reduce the number of measurement antennas while preserving high spatial resolution. In this paper, we deal with the reconstruction of a 3D target scene in the array near-field. By modeling the depth dimension of the target as a property of the reflection coefficients, we can transform the problem of 3D target recovery into an equivalent 2D recovery task. In turn, we have to convert the measurement model to a distributed compressive sensing model to preserve the linearity of the measurement model. For this alternative problem, we develop a vector approximate message passing algorithm. We validate our proposed algorithm through numerical investigations.

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Vector Approximate Message Passing for 3D MIMO Radar

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Keywords

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