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Chaotic signal denoising algorithm based on sparse decomposition |
Jin-Wang Huang(黄锦旺)1, Shan-Xiang Lv(吕善翔)2, Zu-Sheng Zhang(张足生)1, Hua-Qiang Yuan(袁华强)1 |
1 School of Cyberspace Science, Dongguan University of Technology, Dongguan 523808, China; 2 College of Cyber Security, Jinan University, Guangzhou 510632, China |
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Abstract Denoising of chaotic signal is a challenge work due to its wide-band and noise-like characteristics. The algorithm should make the denoised signal have a high signal to noise ratio and retain the chaotic characteristics. We propose a denoising method of chaotic signals based on sparse decomposition and K-singular value decomposition (K-SVD) optimization. The observed signal is divided into segments and decomposed sparsely. The over-complete atomic library is constructed according to the differential equation of chaotic signals. The orthogonal matching pursuit algorithm is used to search the optimal matching atom. The atoms and coefficients are further processed to obtain the globally optimal atoms and coefficients by K-SVD. The simulation results show that the denoised signals have a higher signal to noise ratio and better preserve the chaotic characteristics.
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Received: 27 February 2020
Revised: 10 April 2020
Accepted manuscript online:
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PACS:
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05.45.-a
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(Nonlinear dynamics and chaos)
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05.40.Ca
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(Noise)
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Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61872083) and the Natural Science Foundation of Guangdong Province, China (Grant Nos. 2017A030310659 and 2019A1515011123). |
Corresponding Authors:
Hua-Qiang Yuan
E-mail: yuanhq@dgut.edu.cn
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Cite this article:
Jin-Wang Huang(黄锦旺), Shan-Xiang Lv(吕善翔), Zu-Sheng Zhang(张足生), Hua-Qiang Yuan(袁华强) Chaotic signal denoising algorithm based on sparse decomposition 2020 Chin. Phys. B 29 060505
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[1] |
Alamodi A O A, Sun K H, Ai W, Chen C and Peng D 2019 Chin. Phys. B 28 020503
|
[2] |
Bi H Y, Qi G Y, Hu J B and Wu Q L 2020 Chin. Phys. B 29 020502
|
[3] |
Gao F, Li W Q, Tong H Q and Li Q L 2019 Chin. Phys. B 28 090501
|
[4] |
Wang W B, Zhang X D, Chang Y C, Wang X L, Wang Z, Chen X and Zheng L 2015 Chin. Phys. B 25 010202
|
[5] |
Yang H, Li Y A and Li G H 2015 Acta Armamentarii 36 2330 (in Chinese)
|
[6] |
Liu K, Li H, Dai X C and Xu P X 2008 JEIT 30 1849 (in Chinese)
|
[7] |
Wang M J, Zhou Z Q, Li Z J and Zeng Y C 2018 Acta Phys. Sin. 67 060501 (in Chinese)
|
[8] |
Lv S X and Feng J C 2013 Acta Phys. Sin. 62 230503 (in Chinese)
|
[9] |
Wang W B, Jin Y Y, Wang B, Li W G and Wang X L 2018 Acta Electron. Sin. 46 1652 (in Chinese)
|
[10] |
Hai S, Gao H W and Ruan X J 2016 Int. J. Signal Process., Image Process. and Pattern Recognit. 9 219
|
[11] |
Gao J B, Sultan H, Hu J and Tung W W 2010 IEEE Signal Process. Lett. 17 237
|
[12] |
Tung W W, Gao J B, Hu J and Yang L 2011 Phys. Rev. E 83 046210
|
[13] |
Wu Z H and Huang N E 2010 Adv. Adap. Data Anal. 2 397
|
[14] |
Wang M J, Zhou Z Q, Li Z J and Zeng Y C 2019 Circ. Syst. Signal Proces. 38 2471
|
[15] |
Donoho D L, Elad M and Temlyakov V N 2006 IEEE Trans. Inf. Theory 52 6
|
[16] |
Tseng P 2009 IEEE Trans. Inf. Theory 55 888
|
[17] |
Dumitrescu B and Irofti P 2017 IEEE Signal Proces. Lett. 24 309
|
[18] |
Karlovitz L A 1970 J. Appr. Theory 3 123
|
[19] |
Chen S S, Donoho D L and Saunders M A 2001 SIAM Rev. 43 129
|
[20] |
Cai T T and Wang L 2011 IEEE Trans. Inf. Theory 57 4680
|
[21] |
Karahanoglu N B and Erdogan H 2012 Digital Signal Proces.: A Rev. J. 22 555
|
[22] |
Tian J and Chen L 2012 IEEE Signal Proces. Lett. 19 395
|
[23] |
Chen P, Rong Y, Nordholm S and He Z Q 2017 IEEE Trans. Vehic. Technol. 66 10567
|
[24] |
Lee D J 2016 IEEE Commun. Lett. 20 2115
|
[25] |
Needell D and Vershynin R 2010 IEEE J. Sel. Top. Signal Proces. 4 310
|
[26] |
Chowdhury M S H, Hashim I and Momani S 2009 Chaos Soliton. Fract. 40 1929
|
[27] |
Lv S X, Wang Z S, Hu Z H and Feng J C 2014 Chin. Phys. B 23 010506
|
[28] |
Holger K and Thomas S 2004 Nonlinear Time Series Analysis (Cambridge: Cambridge University Press) pp. 65-74
|
[29] |
Elad M and Aharon M 2006 IEEE Trans. Image Proces. 15 3736
|
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