Total Variation Compressive Sensing for Extended Targets in MIMO Radar

Ignacio Roldan; Francesco Fioranelli; Alexander Yarovoy

doi:10.1109/SAM53842.2022.9827822

Total Variation Compressive Sensing for Extended Targets in MIMO Radar

Ignacio Roldan, Francesco Fioranelli, Alexander Yarovoy

Microwave Sensing, Signals & Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Abstract

The problem of extended target cross-section estimation has been considered. A two-step method based on the Total Variation Compressive Sensing theory has been proposed to solve it. First, a coarse estimation of the target cross-section is performed with classical beamforming methods, and then Compressive Sensing algorithms have been applied to refine it. To the best of the authors' knowledge, this is the first time this approach has been applied to automotive radar signals. The method has been verified simulating extended targets as scatter point clouds and computing the response in a uniform rectangular array. Two metrics have been used, the Intersection over Union and a pseudo Integrated Sidelobe Level. Significant improvements in both metrics compared with classical beamforming methods have been demonstrated.

Original language	English
Title of host publication	2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop, SAM 2022
Publisher	IEEE
Pages	61-65
Number of pages	5
ISBN (Electronic)	9781665406338
DOIs	https://doi.org/10.1109/SAM53842.2022.9827822
Publication status	Published - 2022
Event	12th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2022 - Trondheim, Norway Duration: 20 Jun 2022 → 23 Jun 2022

Publication series

Name	Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
Volume	2022-June
ISSN (Electronic)	2151-870X

Conference

Conference	12th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2022
Country/Territory	Norway
City	Trondheim
Period	20/06/22 → 23/06/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Compressive Sensing
Extended Targets
MIMO automotive radar
Total Variation Normalization

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10.1109/SAM53842.2022.9827822

Total_Variation_Compressive_Sensing_for_Extended_Targets_in_MIMO_RadarFinal published version, 512 KB

Cite this

Roldan, I., Fioranelli, F., & Yarovoy, A. (2022). Total Variation Compressive Sensing for Extended Targets in MIMO Radar. In 2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop, SAM 2022 (pp. 61-65). (Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop; Vol. 2022-June). IEEE. https://doi.org/10.1109/SAM53842.2022.9827822

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title = "Total Variation Compressive Sensing for Extended Targets in MIMO Radar",

abstract = "The problem of extended target cross-section estimation has been considered. A two-step method based on the Total Variation Compressive Sensing theory has been proposed to solve it. First, a coarse estimation of the target cross-section is performed with classical beamforming methods, and then Compressive Sensing algorithms have been applied to refine it. To the best of the authors' knowledge, this is the first time this approach has been applied to automotive radar signals. The method has been verified simulating extended targets as scatter point clouds and computing the response in a uniform rectangular array. Two metrics have been used, the Intersection over Union and a pseudo Integrated Sidelobe Level. Significant improvements in both metrics compared with classical beamforming methods have been demonstrated.",

keywords = "Compressive Sensing, Extended Targets, MIMO automotive radar, Total Variation Normalization",

author = "Ignacio Roldan and Francesco Fioranelli and Alexander Yarovoy",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. ; 12th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2022 ; Conference date: 20-06-2022 Through 23-06-2022",

year = "2022",

doi = "10.1109/SAM53842.2022.9827822",

language = "English",

series = "Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop",

publisher = "IEEE",

pages = "61--65",

booktitle = "2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop, SAM 2022",

address = "United States",

}

Roldan, I , Fioranelli, F & Yarovoy, A 2022, Total Variation Compressive Sensing for Extended Targets in MIMO Radar. in 2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop, SAM 2022. Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop, vol. 2022-June, IEEE, pp. 61-65, 12th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2022, Trondheim, Norway, 20/06/22. https://doi.org/10.1109/SAM53842.2022.9827822

Total Variation Compressive Sensing for Extended Targets in MIMO Radar. / Roldan, Ignacio ; Fioranelli, Francesco ; Yarovoy, Alexander.
2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop, SAM 2022. IEEE, 2022. p. 61-65 (Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop; Vol. 2022-June).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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N2 - The problem of extended target cross-section estimation has been considered. A two-step method based on the Total Variation Compressive Sensing theory has been proposed to solve it. First, a coarse estimation of the target cross-section is performed with classical beamforming methods, and then Compressive Sensing algorithms have been applied to refine it. To the best of the authors' knowledge, this is the first time this approach has been applied to automotive radar signals. The method has been verified simulating extended targets as scatter point clouds and computing the response in a uniform rectangular array. Two metrics have been used, the Intersection over Union and a pseudo Integrated Sidelobe Level. Significant improvements in both metrics compared with classical beamforming methods have been demonstrated.

AB - The problem of extended target cross-section estimation has been considered. A two-step method based on the Total Variation Compressive Sensing theory has been proposed to solve it. First, a coarse estimation of the target cross-section is performed with classical beamforming methods, and then Compressive Sensing algorithms have been applied to refine it. To the best of the authors' knowledge, this is the first time this approach has been applied to automotive radar signals. The method has been verified simulating extended targets as scatter point clouds and computing the response in a uniform rectangular array. Two metrics have been used, the Intersection over Union and a pseudo Integrated Sidelobe Level. Significant improvements in both metrics compared with classical beamforming methods have been demonstrated.

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Total Variation Compressive Sensing for Extended Targets in MIMO Radar

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