NDLI: Bi-ISAR sparse imaging algorithm with complex Gaussian scale mixture prior

Content Provider	IET Digital Library
Author	Zhu, Xiaoxiu Shi, Lin Guo, Baofeng Hu, Wenhua Shang, Chaoxuan Han, Ning
Abstract	The performance of both autofocusing and imaging resolution degrades using the traditional autofocusing and range–Doppler algorithm for bistatic inverse synthetic aperture radar (Bi-ISAR) with sparse apertures. A Bi-ISAR sparse imaging algorithm based on complex Gaussian scale mixture (CGSM) prior is proposed to jointly achieve the high-resolution imaging and autofocusing. First, a sparse basis matrix with the time-varying bistatic angle is constructed to represent the sparse echo data and the Bi-ISAR joint with autofocusing imaging model is established based on compressed sensing from sparse apertures. Second, the elements of the target image and the noise are assumed to be a CGSM prior with Gaussian distribution, respectively. Finally, the sparse image reconstruction and phase autofocusing are accomplished by the variational Bayesian expectation maximisation method. The proposed algorithm with the full Bayesian inference can obtain a well-focused image without manual adjustments of regularisation parameters. Meanwhile, it can avoid the local minimum and structural errors, due to utilising the statistical information of a posterior. Simulated results of electromagnetic numerical data verify the superiority of the algorithm in autofocusing, sparse imaging and noise suppression performance.
Starting Page	2202
Ending Page	2211
Page Count	10
ISSN	17518784
Volume Number	13
e-ISSN	17518792
Issue Number	Issue 12, Dec (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/13/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2019.0296
Journal	IET Radar, Sonar & Navigation
Publisher Date	2019-08-23
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Autofocusing Imaging Model Bayes Method Bayesian Inference Bi-ISAR Sparse Imaging Algorithm Bistatic Inverse Synthetic Aperture Radar CGSM Prior Complex Gaussian Scale Mixture Prior Compressed Sensing Echo Electromagnetic Compatibility Electromagnetic Numerical Data Expectation-maximisation Algorithm Gaussian Distribution High-resolution Imaging Image And Video Coding Image Denoising Image Reconstruction Image Resolution Interference Noise Suppression Performance Phase Autofocusing Radar Equipment Radar Imaging Radar Resolution Range-Doppler Algorithm Sparse Apertures Sparse Basis Matrix Sparse Echo Data Sparse Image Reconstruction Sparse Matrix Statistical A Posterior Information Statistics Synthetic Aperture Radar System And Application Time-varying Bistatic Angle Variational Bayesian Expectation Maximisation Method
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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