NDLI: Intra-pulse modulation radar signal recognition based on CLDN network

Content Provider	IET Digital Library
Author	Wei, Shunjun Qu, Qizhe Su, Hao Wang, Mou Shi, Jun Hao, Xiaojun
Abstract	Automatic modulation classification of radar signals, which plays a significant role in both civilian and military applications, is researched in this study through a deep learning network. In this study, a novel network combined a shallow convolution neural network (CNN), long short-term memory (LSTM) network and deep neural network (DNN) is proposed to recognise six types of radar signals with different signal-to-noise ratio (SNR) levels from −14 to 20 dB. First, raw signal sequences in the time domain, frequency domain and autocorrelation domain are as input for a shallow CNN. Then the features extracted by CNN will be the input of LSTM network. Finally, DNNs will output the signal modulation types directly. The simulation results demonstrate that the accuracies in autocorrelation domain are all more than 90% at −6 dB and close to 100% when SNR > −2 dB. The recognition performances of the three domains are compared. Compared with other recognition methods, the proposed method has higher average accuracy and better performance under low SNR condition. The measured results show that the proposed method has achieved high accuracies of common four kinds of measured radar signals.
Starting Page	803
Ending Page	810
Page Count	8
ISSN	17518784
Volume Number	14
e-ISSN	17518792
Issue Number	Issue 6, Jun (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/14/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2019.0436
Journal	IET Radar, Sonar & Navigation
Publisher Date	2019-12-10
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Autocorrelation Domain Automatic Modulation Classification Civilian Application CLDN Network Convolutional Neural Nets Deep Learning Network Deep Neural Network Digital Signal Processing Feature Extraction Frequency Domain Intra-pulse Modulation Radar Signal Recognition Knowledge Engineering Technique Learning in AI LSTM Network Military Application Modulation Neural Computing Technique Pulse Modulation Radar Signal Radar Signal Processing Raw Signal Sequence Recognition Method Recognition Performances Recurrent Neural Nets Shallow CNN Shallow Convolution Neural Network Short-term Memory Network Signal Classification Signal Modulation Types Signal Processing And Detection Signal to Noise Ratio Statistics Time Domain
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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