A Sparsity Based CFAR Algorithm for Dense Radar Targets

Jingxuan Chen; Shenghua Zhou; Pramod K. Varshney; Jing Lu; Jibin Zheng; Hongwei Liu; Hongtao Su

doi:10.1109/RadarConf2043947.2020.9266526

A Sparsity Based CFAR Algorithm for Dense Radar Targets

Jingxuan Chen, Shenghua Zhou, Pramod K. Varshney, Jing Lu, Jibin Zheng, Hongwei Liu, Hongtao Su

Research output: Chapter in Book/Entry/Poem › Conference contribution

7 Scopus citations

Abstract

Conventional constant false alarm rate (CFAR) radar target detection algorithms, such as the cell-averaging (CA) CFAR and the order statistic (OS) CFAR, performs poorly with dense targets. In this paper, we develop a sparse signal processing-based CFAR algorithm for dense target detection. Under the ℓ1-norm constraint, we develop the relationship between the coefficient and the false alarm rate and verify the false alarm rate via numerical simulations. Numerical results indicate that the sparse signal processing-based method performs better in dense targets scenarios than CA-CFAR and OS-CFAR. The computation cost is evaluated both theoretically and numerically. We show that the computation time of MM-CFAR and CA-CFAR algorithms increases nearly linearly with the sample size.

Original language	English (US)
Title of host publication	2020 IEEE Radar Conference, RadarConf 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728189420
DOIs	https://doi.org/10.1109/RadarConf2043947.2020.9266526
State	Published - Sep 21 2020
Externally published	Yes
Event	2020 IEEE Radar Conference, RadarConf 2020 - Florence, Italy Duration: Sep 21 2020 → Sep 25 2020

Publication series

Name	IEEE National Radar Conference - Proceedings
Volume	2020-September
ISSN (Print)	1097-5659

Conference

Conference	2020 IEEE Radar Conference, RadarConf 2020
Country/Territory	Italy
City	Florence
Period	9/21/20 → 9/25/20

Keywords

CFAR
Sparse signal processing
majorization-minimization
radar target detection

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/RadarConf2043947.2020.9266526

Cite this

Chen, J., Zhou, S., Varshney, P. K., Lu, J., Zheng, J., Liu, H., & Su, H. (2020). A Sparsity Based CFAR Algorithm for Dense Radar Targets. In 2020 IEEE Radar Conference, RadarConf 2020 Article 9266526 (IEEE National Radar Conference - Proceedings; Vol. 2020-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RadarConf2043947.2020.9266526

A Sparsity Based CFAR Algorithm for Dense Radar Targets. / Chen, Jingxuan; Zhou, Shenghua; Varshney, Pramod K. et al.
2020 IEEE Radar Conference, RadarConf 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9266526 (IEEE National Radar Conference - Proceedings; Vol. 2020-September).

Research output: Chapter in Book/Entry/Poem › Conference contribution

Chen, J, Zhou, S, Varshney, PK, Lu, J, Zheng, J, Liu, H & Su, H 2020, A Sparsity Based CFAR Algorithm for Dense Radar Targets. in 2020 IEEE Radar Conference, RadarConf 2020., 9266526, IEEE National Radar Conference - Proceedings, vol. 2020-September, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE Radar Conference, RadarConf 2020, Florence, Italy, 9/21/20. https://doi.org/10.1109/RadarConf2043947.2020.9266526

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title = "A Sparsity Based CFAR Algorithm for Dense Radar Targets",

abstract = "Conventional constant false alarm rate (CFAR) radar target detection algorithms, such as the cell-averaging (CA) CFAR and the order statistic (OS) CFAR, performs poorly with dense targets. In this paper, we develop a sparse signal processing-based CFAR algorithm for dense target detection. Under the ℓ1-norm constraint, we develop the relationship between the coefficient and the false alarm rate and verify the false alarm rate via numerical simulations. Numerical results indicate that the sparse signal processing-based method performs better in dense targets scenarios than CA-CFAR and OS-CFAR. The computation cost is evaluated both theoretically and numerically. We show that the computation time of MM-CFAR and CA-CFAR algorithms increases nearly linearly with the sample size.",

keywords = "CFAR, Sparse signal processing, majorization-minimization, radar target detection",

author = "Jingxuan Chen and Shenghua Zhou and Varshney, {Pramod K.} and Jing Lu and Jibin Zheng and Hongwei Liu and Hongtao Su",

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AU - Chen, Jingxuan

AU - Zhou, Shenghua

AU - Varshney, Pramod K.

AU - Lu, Jing

AU - Zheng, Jibin

AU - Liu, Hongwei

AU - Su, Hongtao

PY - 2020/9/21

Y1 - 2020/9/21

N2 - Conventional constant false alarm rate (CFAR) radar target detection algorithms, such as the cell-averaging (CA) CFAR and the order statistic (OS) CFAR, performs poorly with dense targets. In this paper, we develop a sparse signal processing-based CFAR algorithm for dense target detection. Under the ℓ1-norm constraint, we develop the relationship between the coefficient and the false alarm rate and verify the false alarm rate via numerical simulations. Numerical results indicate that the sparse signal processing-based method performs better in dense targets scenarios than CA-CFAR and OS-CFAR. The computation cost is evaluated both theoretically and numerically. We show that the computation time of MM-CFAR and CA-CFAR algorithms increases nearly linearly with the sample size.

AB - Conventional constant false alarm rate (CFAR) radar target detection algorithms, such as the cell-averaging (CA) CFAR and the order statistic (OS) CFAR, performs poorly with dense targets. In this paper, we develop a sparse signal processing-based CFAR algorithm for dense target detection. Under the ℓ1-norm constraint, we develop the relationship between the coefficient and the false alarm rate and verify the false alarm rate via numerical simulations. Numerical results indicate that the sparse signal processing-based method performs better in dense targets scenarios than CA-CFAR and OS-CFAR. The computation cost is evaluated both theoretically and numerically. We show that the computation time of MM-CFAR and CA-CFAR algorithms increases nearly linearly with the sample size.

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KW - Sparse signal processing

KW - majorization-minimization

KW - radar target detection

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A Sparsity Based CFAR Algorithm for Dense Radar Targets

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Keywords

ASJC Scopus subject areas

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