NDLI: Radar signals classification using energy-time-frequency distribution features

Content Provider	IET Digital Library
Author	Seddighi, Zahra Ahmadzadeh, Mohammad Reza Taban, Mohammad Reza
Abstract	In this research, the authors extract features from intermediate frequency band radar signals in the time–frequency domain for classification. The extracted features are classified via support vector machine and K-nearest neighbour classifiers. They show the accuracy of classification is above 99% for different classes of radar signals except for frequency shift keying signal with accuracy 83% in negative signal-to-noise ratio (SNR). To identify the radars with the same class, the classification accuracy is 91% for SNR between 5 to 15 dB and 64% in the worst case for SNR between −1 to 10 dB. The proposed method is compared with some methods based on the empirical mode decomposition (EMD), cumulant and Zhao Atlas Mark Distribution (ZAMD). The results show that the classification error in the proposed method is less than that of EMD method 55% in the best case and 9% in the worst case. The performance of the cumulant-based method is weaker than that of the proposed method in common designed scenarios becoming almost similar only in one scenario. The ZAMD-based method could only distinguish the signals with different modulations in high SNR while it is unable to classify the signals with the same modulation but different parameters.
Starting Page	707
Ending Page	715
Page Count	9
ISSN	17518784
Volume Number	14
e-ISSN	17518792
Issue Number	Issue 5, May (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/14/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2019.0331
Journal	IET Radar, Sonar & Navigation
Publisher Date	2020-01-27
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Classification Accuracy Digital Signal Processing Electrical Engineering Computing Electronic Support Energy-time-frequency Distribution Feature Feature Extraction Frequency Shift Higher Order Statistics Intermediate Frequency Band Knowledge Engineering Technique Mathematical Analysis Negative Signal-to-noise Ratio Noise Figure -1.0 DB to 10.0 DB Noise Figure 5.0 DB to 15.0 DB Radar Computing Radar Equipment Radar Signal Classification Radar Signal Processing Received Radar Signal Signal Classification Signal Processing And Detection SNR Statistics Support Vector Machine System And Application Time-frequency Analysis Time–frequency Domain Time–frequency Transformation
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Modulation classification based on denoising autoencoder and convolutional neural network with GNU radio

DNN-based interference mitigation beamformer

Joint carrier frequency and DOA estimation using a modified ULA based MWC discrete compressed sampling receiver

Deep-learning-based beamforming for rejecting interferences

Method to reduce the signal-to-noise ratio required for modulation recognition based on logarithmic properties

Classification of Airborne Radar Signals based on Time-Frequency Features using Wigner-Ville Distribution

Classification of Doppler radar reflections as preprocessing for breathing rate monitoring

Human–vehicle classification using feature-based SVM in 77-GHz automotive FMCW radar

Reduced complexity and near optimum detector for linear-frequency-modulated and phase-modulated LPI radar signals

Radar signals classification using energy-time-frequency distribution features

Similar Documents

Modulation classification based on denoising autoencoder and convolutional neural network with GNU radio

DNN-based interference mitigation beamformer

Joint carrier frequency and DOA estimation using a modified ULA based MWC discrete compressed sampling receiver

Deep-learning-based beamforming for rejecting interferences

Method to reduce the signal-to-noise ratio required for modulation recognition based on logarithmic properties

Classification of Airborne Radar Signals based on Time-Frequency Features using Wigner-Ville Distribution

Classification of Doppler radar reflections as preprocessing for breathing rate monitoring

Human–vehicle classification using feature-based SVM in 77-GHz automotive FMCW radar

Reduced complexity and near optimum detector for linear-frequency-modulated and phase-modulated LPI radar signals

Radar signals classification using energy-time-frequency distribution features