Frequency-Hopping Signal Network-Station Sorting Based on Maxout Network Model and Generative Method

Frequency-Hopping Signal Network-Station Sorting Based on Maxout Network Model and Generative Method

Automatic frequency-hopping (FH) signal network-station sorting is one of the most difficult and import problems in the field of electronic warfare, especially in a complex electromagnetic environment. In this paper, an automatic and reliable network-station sorting method of FH signal with maxout n...

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Main Authors: Hongguang Li, Ying Guo, Ping Sui, Xinyong Yu, Xin Yang, Shaobo Wang
Format: Article
Language:English
Published: Hindawi Limited 2019-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2019/9152728
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spelling doaj-556dd1a0ecd74365840ed0248df3f3942020-11-24T21:54:51ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472019-01-01201910.1155/2019/91527289152728Frequency-Hopping Signal Network-Station Sorting Based on Maxout Network Model and Generative MethodHongguang Li0Ying Guo1Ping Sui2Xinyong Yu3Xin Yang4Shaobo Wang5Institute of Information and Navigation, Air Force Engineering University, Xi’an, Shaanxi, 710077, ChinaInstitute of Information and Navigation, Air Force Engineering University, Xi’an, Shaanxi, 710077, ChinaInstitute of Information and Navigation, Air Force Engineering University, Xi’an, Shaanxi, 710077, ChinaAir Force Communication NCO Academy, Dalian, Shenyang, 116600, ChinaInstitute of Information and Navigation, Air Force Engineering University, Xi’an, Shaanxi, 710077, ChinaInstitute of Information and Navigation, Air Force Engineering University, Xi’an, Shaanxi, 710077, ChinaAutomatic frequency-hopping (FH) signal network-station sorting is one of the most difficult and import problems in the field of electronic warfare, especially in a complex electromagnetic environment. In this paper, an automatic and reliable network-station sorting method of FH signal with maxout network feature extraction and generative-based classification method is proposed. Experiments on real FH data sets demonstrate that the proposed method not only outperforms the competitive feature extraction methods with a higher accuracy of FH signal network-station sorting but also has a better robustness against noise, especially Gaussian noise.http://dx.doi.org/10.1155/2019/9152728
collection DOAJ
language English
format Article
sources DOAJ
author Hongguang Li
Ying Guo
Ping Sui
Xinyong Yu
Xin Yang
Shaobo Wang
spellingShingle Hongguang Li
Ying Guo
Ping Sui
Xinyong Yu
Xin Yang
Shaobo Wang
Frequency-Hopping Signal Network-Station Sorting Based on Maxout Network Model and Generative Method
Mathematical Problems in Engineering
author_facet Hongguang Li
Ying Guo
Ping Sui
Xinyong Yu
Xin Yang
Shaobo Wang
author_sort Hongguang Li
title Frequency-Hopping Signal Network-Station Sorting Based on Maxout Network Model and Generative Method
title_short Frequency-Hopping Signal Network-Station Sorting Based on Maxout Network Model and Generative Method
title_full Frequency-Hopping Signal Network-Station Sorting Based on Maxout Network Model and Generative Method
title_fullStr Frequency-Hopping Signal Network-Station Sorting Based on Maxout Network Model and Generative Method
title_full_unstemmed Frequency-Hopping Signal Network-Station Sorting Based on Maxout Network Model and Generative Method
title_sort frequency-hopping signal network-station sorting based on maxout network model and generative method
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2019-01-01
description Automatic frequency-hopping (FH) signal network-station sorting is one of the most difficult and import problems in the field of electronic warfare, especially in a complex electromagnetic environment. In this paper, an automatic and reliable network-station sorting method of FH signal with maxout network feature extraction and generative-based classification method is proposed. Experiments on real FH data sets demonstrate that the proposed method not only outperforms the competitive feature extraction methods with a higher accuracy of FH signal network-station sorting but also has a better robustness against noise, especially Gaussian noise.
url http://dx.doi.org/10.1155/2019/9152728
work_keys_str_mv AT hongguangli frequencyhoppingsignalnetworkstationsortingbasedonmaxoutnetworkmodelandgenerativemethod
AT yingguo frequencyhoppingsignalnetworkstationsortingbasedonmaxoutnetworkmodelandgenerativemethod
AT pingsui frequencyhoppingsignalnetworkstationsortingbasedonmaxoutnetworkmodelandgenerativemethod
AT xinyongyu frequencyhoppingsignalnetworkstationsortingbasedonmaxoutnetworkmodelandgenerativemethod
AT xinyang frequencyhoppingsignalnetworkstationsortingbasedonmaxoutnetworkmodelandgenerativemethod
AT shaobowang frequencyhoppingsignalnetworkstationsortingbasedonmaxoutnetworkmodelandgenerativemethod
_version_ 1725865365000421376

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