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Feature-supervised Compressed Sensing for Microwave Imaging Systems

Lookup NU author(s): Chaoqing Tang, Professor Gui Yun TianORCiD, Professor Said Boussakta, Dr Jianbo Wu



This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2020.

For re-use rights please refer to the publisher's terms and conditions.


IEEE. Traditional compressed sensing (CS) applications use sparse information for down-sampling but ignore overall system objectives such as feature extraction. This paper jointly designs sensing and feature extraction process to improve efficiency of microwave imaging systems in time, storage, and feature extraction. A feature-supervised compressed sensing (FsCS) is proposed for cases where not all data contribute to feature extraction. Compared to traditional spatial-spectral sweep and CS solutions, a feature constraint is added in designing CS measurement matrix. More efficient sensing and feature extraction are achieved because only the data contributing to feature extraction is sampled and reconstructed. To improve time efficiency of CS reconstruction, an aligned spatial-spectral sensing (ASSS) is involved in FsCS to enable joint reconstruction. The proposed scheme is validated in an open-ended waveguide imaging system for low-energy impact damage feature detection. The experimental results demonstrate one order of magnitude improve in time and two orders of improvement in data compression ratio compare to state-of-the-art method while preserving interested feature. This paper can inspire joint sensing-processing designs for more intelligent industrial processes.

Publication metadata

Author(s): Tang C, Tian G, Boussakta S, Wu J

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Instrumentation and Measurement

Year: 2020

Volume: 69

Issue: 8

Pages: 5287-5297

Print publication date: 01/08/2020

Online publication date: 26/12/2019

Acceptance date: 02/04/2018

Date deposited: 13/01/2020

ISSN (print): 0018-9456

ISSN (electronic): 1557-9662

Publisher: IEEE


DOI: 10.1109/TIM.2019.2962562


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