NDLI: Emitter signals modulation recognition based on discriminative projection and collaborative representation

Content Provider	IET Digital Library
Author	Li, Dongjin Yang, Ruijuan Dong, Ruijie Zuo, Jiajun
Abstract	To enhance the modulation recognition performance of emitter signals under low signal-to-noise ratio (SNR), a recognition system based on secondary time–frequency distribution, discriminative projection, and collaborative representation is proposed. Firstly, a novel time–frequency processing method, including sparse-domain noise reduction and secondary feature extraction, is proposed to reduce noise interference and information redundancy in time–frequency images. In this way, secondary time–frequency distribution with high stability and detailed representation is obtained. Then, the classifier based on discriminative projection and collaborative representation was designed to enhance the ability of low-dimensional representation and between-class discrimination, which optimised using the mini-batch random gradient descent method. As shown in the simulation, the overall average recognition success rate of this system aiming at eight types of emitter signals reaches 95.6% at the SNR of −8 dB. Results of simulation and analysis indicate the superiority of the proposed classification system in terms of robustness, timeliness, and adaptability.
Starting Page	782
Ending Page	791
Page Count	10
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.2019.0550
Journal	IET Radar, Sonar & Navigation
Publisher Date	2020-02-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Average Recognition Success Rate Between-class Discrimination Collaborative Representation Detailed Representation Discriminative Projection Emitter Signal Modulation Recognition Feature Extraction Gradient Method Information Redundancy Low Signal-to-noise Ratio Low-dimensional Representation Mathematical Analysis Mini-batch Random Gradient Descent Method Modulation Modulation Recognition Performance Noise Interference Optimisation Technique Recognition System Secondary Feature Extraction Secondary Time–frequency Distribution Signal Classification Signal Processing And Detection Signal Processing Theory Signal Representation SNR Sparse-domain Noise Reduction Time-frequency Analysis Time–frequency Images Time–frequency Processing Method
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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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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