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Period Estimation of Spread Spectrum Codes Based on ResNet

In order to more effectively monitor and interfere with enemy signals, it is particularly important to accurately and efficiently identify the intercepted signals and estimate their parameters in the increasingly complex electromagnetic environment. Therefore, in non-cooperative situations, it is of...

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Detalles Bibliográficos
Autores principales: Gu, Han-Qing, Liu, Xia-Xia, Xu, Lu, Zhang, Yi-Jia, Lu, Zhe-Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422606/
https://www.ncbi.nlm.nih.gov/pubmed/37571785
http://dx.doi.org/10.3390/s23157002
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author Gu, Han-Qing
Liu, Xia-Xia
Xu, Lu
Zhang, Yi-Jia
Lu, Zhe-Ming
author_facet Gu, Han-Qing
Liu, Xia-Xia
Xu, Lu
Zhang, Yi-Jia
Lu, Zhe-Ming
author_sort Gu, Han-Qing
collection PubMed
description In order to more effectively monitor and interfere with enemy signals, it is particularly important to accurately and efficiently identify the intercepted signals and estimate their parameters in the increasingly complex electromagnetic environment. Therefore, in non-cooperative situations, it is of great practical significance to study how to accurately detect direct sequence spread spectrum (DSSS) signals in real time and estimate their parameters. The traditional time-delay correlation algorithm encounters the challenges such as peak energy leakage and false peak interference. As an alternative, this paper introduces a Pseudo-Noise (PN) code period estimation method utilizing a one-dimensional (1D) convolutional neural network based on the residual network (CNN-ResNet). This method transforms the problem of spread spectrum code period estimation into a multi-classification problem of spread spectrum code length estimation. Firstly, the In-phase/Quadrature(I/Q) two-way of the received DSSS signals is directly input into the CNN-ResNet model, which will automatically learn the characteristics of the DSSS signal with different PN code lengths and then estimate the PN code length. Simulation experiments are conducted using a data set with DSSS signals ranging from −20 to 10 dB in terms of signal-to-noise ratios (SNRs). Upon training and verifying the model using BPSK modulation, it is then put to the test with QPSK-modulated signals, and the estimation performance was analyzed through metrics such as loss function, accuracy rate, recall rate, and confusion matrix. The results demonstrate that the 1D CNN-ResNet proposed in this paper is capable of effectively estimating the PN code period of the non-cooperative DSSS signal, exhibiting robust generalization abilities.
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spelling pubmed-104226062023-08-13 Period Estimation of Spread Spectrum Codes Based on ResNet Gu, Han-Qing Liu, Xia-Xia Xu, Lu Zhang, Yi-Jia Lu, Zhe-Ming Sensors (Basel) Article In order to more effectively monitor and interfere with enemy signals, it is particularly important to accurately and efficiently identify the intercepted signals and estimate their parameters in the increasingly complex electromagnetic environment. Therefore, in non-cooperative situations, it is of great practical significance to study how to accurately detect direct sequence spread spectrum (DSSS) signals in real time and estimate their parameters. The traditional time-delay correlation algorithm encounters the challenges such as peak energy leakage and false peak interference. As an alternative, this paper introduces a Pseudo-Noise (PN) code period estimation method utilizing a one-dimensional (1D) convolutional neural network based on the residual network (CNN-ResNet). This method transforms the problem of spread spectrum code period estimation into a multi-classification problem of spread spectrum code length estimation. Firstly, the In-phase/Quadrature(I/Q) two-way of the received DSSS signals is directly input into the CNN-ResNet model, which will automatically learn the characteristics of the DSSS signal with different PN code lengths and then estimate the PN code length. Simulation experiments are conducted using a data set with DSSS signals ranging from −20 to 10 dB in terms of signal-to-noise ratios (SNRs). Upon training and verifying the model using BPSK modulation, it is then put to the test with QPSK-modulated signals, and the estimation performance was analyzed through metrics such as loss function, accuracy rate, recall rate, and confusion matrix. The results demonstrate that the 1D CNN-ResNet proposed in this paper is capable of effectively estimating the PN code period of the non-cooperative DSSS signal, exhibiting robust generalization abilities. MDPI 2023-08-07 /pmc/articles/PMC10422606/ /pubmed/37571785 http://dx.doi.org/10.3390/s23157002 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gu, Han-Qing
Liu, Xia-Xia
Xu, Lu
Zhang, Yi-Jia
Lu, Zhe-Ming
Period Estimation of Spread Spectrum Codes Based on ResNet
title Period Estimation of Spread Spectrum Codes Based on ResNet
title_full Period Estimation of Spread Spectrum Codes Based on ResNet
title_fullStr Period Estimation of Spread Spectrum Codes Based on ResNet
title_full_unstemmed Period Estimation of Spread Spectrum Codes Based on ResNet
title_short Period Estimation of Spread Spectrum Codes Based on ResNet
title_sort period estimation of spread spectrum codes based on resnet
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422606/
https://www.ncbi.nlm.nih.gov/pubmed/37571785
http://dx.doi.org/10.3390/s23157002
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