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

收稿日期:2020-07-03 接受日期:2021-11-10 出版日期:2022-01-05 发布日期:2022-01-05

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).

摘要/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

. [J]. Journal of Systems Engineering and Electronics, 2021, 32(6): 1345-1353.

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.

图/表 7

参考文献 31

1	BEYGI S, ALALI S, MALEKI A, et al An efficient algorithm for compression-based compressed sensing. Information and Inference, 2019, 8 (2): 343- 375. doi: 10.1093/imaiai/iay014
2	BOYER C, BIGOT J, WEISS P Compressed sensing with structured sparsity and structured acquisition. Applied and Computational Harmonic Analysis, 2019, 46 (2): 312- 350. doi: 10.1016/j.acha.2017.05.005
3	SANDEEP V, NEETU S, AJAY K S Genetic algorithm-based optimized cluster head selection for single and multiple data sinks in heterogeneous wireless sensor network. Applied Soft Computing, 2019, 85, 105788. doi: 10.1016/j.asoc.2019.105788
4	SANDEEP V, NEETU S, AJAY K S A novelistic approach for energy efficient routing using single and multiple data sinks in heterogeneous wireless sensor network. Peer-to-Peer Networking and Applications, 2019, 12 (5): 1110- 1136. doi: 10.1007/s12083-019-00777-5
5	MARK A D, ANDREW K M, DEANNA N, et al Constrained adaptive sensing. IEEE Trans. on Signal Processing, 2016, 46 (2): 5437- 5449.
6	SANDEEP V, RICHA M, DIVYA S, et al Wireless sensor network and hierarchical routing protocols: a review. International Journal of Computer Trends and Technology, 2013, 4 (8): 2411- 2416.
7	THOMAS B, MIKE E D. Iterative hard thresholding for compressed sensing. Applied and Computational Harmonic Analysis, 2009, 27 (3): 265- 274. doi: 10.1016/j.acha.2009.04.002
8	TROPP J A, GILBERT A C Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. on Information Theory, 2007, 53 (12): 4655- 4666. doi: 10.1109/TIT.2007.909108
9	DEANNA N, ROMAN V Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit. Foundations of Computational Mathematics, 2019, 9 (3): 317- 334.
10	DEANNA N, ROMAN V Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit. IEEE Journal of Selected Topics in Signal Processing, 2010, 4 (2): 310- 316. doi: 10.1109/JSTSP.2010.2042412
11	LIU E, VLADIMIR N T The orthogonal super greedy algorithm and applications in compressed sensing. IEEE Trans. on Information Theory, 2011, 58 (4): 2040- 2047.
12	WEI D, OLGICA M Subspace pursuit for compressive sensing signal reconstruction. IEEE Trans. on Information Theory, 2009, 55 (5): 2230- 2249. doi: 10.1109/TIT.2009.2016006
13	QUN M, SHEN Y A remark on the restricted isometry property in orthogonal matching pursuit. IEEE Trans. on Information Theory, 2012, 58 (6): 3654- 3656. doi: 10.1109/TIT.2012.2185923
14	LJUBISA S, MILOS B Analysis of the reconstruction of sparse signals in the DCT domain applied to audio signals. IEEE/ACM Trans. on Audio, Speech, and Language Processing, 2018, 26 (7): 1220- 1235. doi: 10.1109/TASLP.2018.2819819
15	MADYCH W R. Solutions of underdetermined systems of linear equations. Lecture Notes-Monograph Series, 1991, 20(3): 227−238.
16	NEEDELL D, TROPP J A CoSaMP: iterative signal recovery from incomplete and inaccurate samples. Applied and Computational Harmonic Analysis, 2009, 26 (3): 301- 321. doi: 10.1016/j.acha.2008.07.002
17	THONG T D, LU G, NAM N, et al Sparsity adaptive matching pursuit algorithm for practical compressed sensing. Proc. of the IEEE Conference on Signals, Systems and Computers, 2009, 581- 587.
18	NI J M, SUN Q Z, LU J M A modified adaptive compressive sampling matching pursuit algorithm. Communications Technology, 2016, 49 (8): 992- 996.
19	JOHN Z S, VIVEK K G Optimal quantization of random measurements in compressed sensing. Proc. of the IEEE International Symposium on Information Theory, 2009, 6- 10.
20	LUO Y J, GUI G, CONG X C, et al Sparse estimation based on a new random regularized matching pursuit generalized approximate message passing algorithm. Entropy, 2016, 18 (6): 207. doi: 10.3390/e18060207
21	WANG X, ZHANG Y X, HUANG Z Q Regularized backtracking adaptive pursuit algorithm based variable step-size. Acta Electronic Sinica, 2018, 46 (8): 1829- 1834.
22	JARVIS H, RUI M C, ROBERT N Distilled sensing: adaptive sampling for sparse detection and estimation. IEEE Trans. on Information Theory, 2011, 57 (9): 6222- 6235. doi: 10.1109/TIT.2011.2162269
23	WANG Z R, LI B, LIU N H, et al. Distilling knowledge from an ensemble of convolutional neural networks for seismic fault detection. IEEE Geoscience and Remote Sensing Letters. DOI: 10.1109/LGRS.2020.3034960.
24	JARVIS D H, RICHARD G B, RUI M C, et al Compressive distilled sensing: sparse recovery using adaptivity in compressive measurements. Proc. of the 43rd Asilomar Conference on Signals, Systems and Computers, 2009, 1551- 1555.
25	SHEN Z Y, CHEN X M, WANG Q Q A cooperative construction method for the measurement matrix and sensing dictionary used in compression sensing. EURASIP Journal on Advances in Signal Processing, 2010, 10, 1- 8. doi: 10.1186/s13634-020-0661-1
26	KARIN S, PIERRE V Dictionary preconditioning for greedy algorithms. IEEE Trans. on Signal Processing, 2008, 56 (5): 1994- 2002. doi: 10.1109/TSP.2007.911494
27	LI J. Research on deterministic measurement matrix and sparse recovery algorithm with applications to WCSS. Harbin, China: Harbin Institute of Technology, 2016. (in Chinese)
28	WANG J, SEOKBEOP K, BYONGHYO S Generalized orthogonal matching pursuit. IEEE Trans. on Signal Processing, 2012, 60 (12): 6202- 6216. doi: 10.1109/TSP.2012.2218810
29	LJUBISA S, DANILO P M, MILOS D, et al Demystifying the coherence index in compressive sensing. IEEE Signal Processing Magazine, 2020, 37 (1): 152- 162. doi: 10.1109/MSP.2019.2945080
30	ZHANG L Q, HUANG L, LI B Adaptive sensing matrix design for greedy algorithms in MMV compressive sensing. Proc. of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing, 2019, 5571- 5575.
31	LI B. Research on generalized OMP algorithm and dictionaries construction in compressive sensing. Harbin, China: Harbin Institute of Technology, 2015. (in Chinese)

A sparsity adaptive compressed signal reconstruction based on sensing dictionary

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