ISAR Imaging Based on Probabilistic Pattern-Coupled Sparse Bayesian Learning

Juan Zhao; Xia Bai; Zichen Ning

doi:10.1109/ICSIDP62679.2024.10868695

ISAR Imaging Based on Probabilistic Pattern-Coupled Sparse Bayesian Learning

Juan Zhao^*
, Xia Bai
, Zichen Ning

^*Corresponding author for this work

School of Information and Electronics

The 54th Research Institute of Cetc

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

Abstract

In this paper we consider the inverse synthetic aperture radar (ISAR) imaging. To obtain high resolution target images, a novel probabilistic pattern-coupled sparse Bayesian learning (P-PCSBL) algorithm is proposed, which uses a probabilistic coupled prior model to represent the block sparse structural characteristics of ISAR images. The P-PCSBL algorithm is derived by variational Bayesian inference technique, where a decay factor is introduced to make the reconstructed signal sparser, thereby enabling the P-PCSBL to have the ability of suppressing noise. Simulation experiments demonstrate that the P-PCSBL has robust block sparse recovery performance and can obtain high quality ISAR images under low signal-to-noise ratio.

Original language	English
Title of host publication	IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798331515669
DOIs	https://doi.org/10.1109/ICSIDP62679.2024.10868695
Publication status	Published - 2024
Event	2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024 - Zhuhai, China Duration: 22 Nov 2024 → 24 Nov 2024

Publication series

Name	IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

Conference

Conference	2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
Country/Territory	China
City	Zhuhai
Period	22/11/24 → 24/11/24

Keywords

block sparse recovery
inverse synthetic aperture radar
sparse Bayesian learning

Access to Document

10.1109/ICSIDP62679.2024.10868695

Cite this

Zhao, J., Bai, X., & Ning, Z. (2024). ISAR Imaging Based on Probabilistic Pattern-Coupled Sparse Bayesian Learning. In IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024 (IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSIDP62679.2024.10868695

@inproceedings{f9f558a8851248b4bdd2392bd0cdfefa,

title = "ISAR Imaging Based on Probabilistic Pattern-Coupled Sparse Bayesian Learning",

abstract = "In this paper we consider the inverse synthetic aperture radar (ISAR) imaging. To obtain high resolution target images, a novel probabilistic pattern-coupled sparse Bayesian learning (P-PCSBL) algorithm is proposed, which uses a probabilistic coupled prior model to represent the block sparse structural characteristics of ISAR images. The P-PCSBL algorithm is derived by variational Bayesian inference technique, where a decay factor is introduced to make the reconstructed signal sparser, thereby enabling the P-PCSBL to have the ability of suppressing noise. Simulation experiments demonstrate that the P-PCSBL has robust block sparse recovery performance and can obtain high quality ISAR images under low signal-to-noise ratio.",

keywords = "block sparse recovery, inverse synthetic aperture radar, sparse Bayesian learning",

author = "Juan Zhao and Xia Bai and Zichen Ning",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024 ; Conference date: 22-11-2024 Through 24-11-2024",

year = "2024",

doi = "10.1109/ICSIDP62679.2024.10868695",

language = "English",

series = "IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024",

address = "United States",

}

Zhao, J , Bai, X & Ning, Z 2024, ISAR Imaging Based on Probabilistic Pattern-Coupled Sparse Bayesian Learning. in IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024. IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024, Institute of Electrical and Electronics Engineers Inc., 2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024, Zhuhai, China, 22/11/24. https://doi.org/10.1109/ICSIDP62679.2024.10868695

ISAR Imaging Based on Probabilistic Pattern-Coupled Sparse Bayesian Learning. / Zhao, Juan ; Bai, Xia; Ning, Zichen.
IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024. Institute of Electrical and Electronics Engineers Inc., 2024. (IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024).

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

TY - GEN

T1 - ISAR Imaging Based on Probabilistic Pattern-Coupled Sparse Bayesian Learning

AU - Zhao, Juan

AU - Bai, Xia

AU - Ning, Zichen

PY - 2024

Y1 - 2024

N2 - In this paper we consider the inverse synthetic aperture radar (ISAR) imaging. To obtain high resolution target images, a novel probabilistic pattern-coupled sparse Bayesian learning (P-PCSBL) algorithm is proposed, which uses a probabilistic coupled prior model to represent the block sparse structural characteristics of ISAR images. The P-PCSBL algorithm is derived by variational Bayesian inference technique, where a decay factor is introduced to make the reconstructed signal sparser, thereby enabling the P-PCSBL to have the ability of suppressing noise. Simulation experiments demonstrate that the P-PCSBL has robust block sparse recovery performance and can obtain high quality ISAR images under low signal-to-noise ratio.

AB - In this paper we consider the inverse synthetic aperture radar (ISAR) imaging. To obtain high resolution target images, a novel probabilistic pattern-coupled sparse Bayesian learning (P-PCSBL) algorithm is proposed, which uses a probabilistic coupled prior model to represent the block sparse structural characteristics of ISAR images. The P-PCSBL algorithm is derived by variational Bayesian inference technique, where a decay factor is introduced to make the reconstructed signal sparser, thereby enabling the P-PCSBL to have the ability of suppressing noise. Simulation experiments demonstrate that the P-PCSBL has robust block sparse recovery performance and can obtain high quality ISAR images under low signal-to-noise ratio.

KW - block sparse recovery

KW - inverse synthetic aperture radar

KW - sparse Bayesian learning

UR - https://www.scopus.com/pages/publications/86000018950

U2 - 10.1109/ICSIDP62679.2024.10868695

DO - 10.1109/ICSIDP62679.2024.10868695

M3 - Conference contribution

AN - SCOPUS:86000018950

T3 - IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

BT - IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

Y2 - 22 November 2024 through 24 November 2024

ER -

Zhao J , Bai X, Ning Z. ISAR Imaging Based on Probabilistic Pattern-Coupled Sparse Bayesian Learning. In IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024. Institute of Electrical and Electronics Engineers Inc. 2024. (IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024). doi: 10.1109/ICSIDP62679.2024.10868695

ISAR Imaging Based on Probabilistic Pattern-Coupled Sparse Bayesian Learning

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