A sparsity adaptive compressed signal reconstruction based on sensing dictionary

doi:10.23919/JSEE.2021.000114

Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (6): 1345-1353.doi: 10.23919/JSEE.2021.000114

• ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

A sparsity adaptive compressed signal reconstruction based on sensing dictionary

Zhiyuan SHEN^1,*(), Qianqian WANG^1,²(), Xinmiao CHENG^1,³()

¹ College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
² Zhejiang Scientific Research Institute of Transport, Hangzhou 310023, China
³ Civil Aviation Branch, Jiangsu Transportation Institute, Nanjing 210003, China

Received:2020-07-03 Accepted:2021-11-10 Online:2022-01-05 Published:2022-01-05
Contact: Zhiyuan SHEN E-mail:shenzy@nuaa.edu.cn;arya@nuaa.edu.cn;459188293@qq.com
About author:|SHEN Zhiyuan was born in 1985. He is an associate professor at Nanjing University of Aeronautics and Astronautics (NUAA), vice director of the Air Traffic Department of the College of Civil Aviation, NUAA. He holds the Certificate of Aeronautic Information and the Certificate of Flight Procedure Design both authorized by the Civil Aviation Administration of China (CAAC). His research interests include air traffic management, airport operation and optimization, digital signal processing, and deep learning.E-mail: shenzy@nuaa.edu.cn||WANG Qianqian was born in 1990. She received her B.S. degree in communication engineering from Nanjing University of Technology in 2015. She received her M.S. degree in transportation engineering from Nanjing University of Aeronautics and Astronautics in 2020. She is now working at Zhejiang Scientific Research Institute of Transport, Hangzhou, China. Her research interests include signal processing technology, multi-source information intelligent processing technology, target detection and recognition, and deep learning. E-mail: arya@nuaa.edu.cn||CHENG Xinmiao was born in 1994. He received his B.S. degree in logistics engineering from Tianjin University in 2016. He received his M.S. degree in transportation engineering from Nanjing University of Aeronautics and Astronautics in 2019. He is a system engineer at the Civil Aviation Branch, Jiangsu Transportation Institute Group. His research interests are new system detection technology, digital signal processing, and airport operation and optimization. E-mail: 459188293@qq.com
Supported by:
This work was supported by the National Natural Science Foundation of China (61773202;71874081), the Special Financial Grant from China Postdoctoral Science Foundation (2017T100366), the Key Laboratory of Avionics System Integrated Technology for National Defense Science and Technology, China Institute of Avionics Radio Electronics (6142505180407), the Open Fund of CAAC Key laboratory of General Aviation Operation, Civil Aviation Management Institute of China (CAMICKFJJ-2019-04), and the Innovation Project of the Civil Aviation Administration of China (EAB19001).

Abstract

Abstract:

Signal reconstruction is a significantly important theoretical issue for compressed sensing. Considering the situation of signal reconstruction with unknown sparsity, the conventional signal reconstruction algorithms usually perform low accuracy. In this work, a sparsity adaptive signal reconstruction algorithm using sensing dictionary is proposed to achieve a lower reconstruction error. The sparsity estimation method is combined with the construction of the support set based on sensing dictionary. Using the adaptive sparsity method, an iterative signal reconstruction algorithm is proposed. The sufficient conditions for the exact signal reconstruction of the algorithm also is proved by theory. According to a series of simulations, the results show that the proposed method has higher precision compared with other state-of-the-art signal reconstruction algorithms especially in a high compression ratio scenarios.

Key words: compressed sensing, signal reconstruction, adaptive sparsity estimation, sensing dictionary

Zhiyuan SHEN, Qianqian WANG, Xinmiao CHENG. A sparsity adaptive compressed signal reconstruction based on sensing dictionary[J]. Journal of Systems Engineering and Electronics, 2021, 32(6): 1345-1353.

Figures/Tables 7

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References 31

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A sparsity adaptive compressed signal reconstruction based on sensing dictionary

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