Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Classification of Signals01:30

Classification of Signals

1.1K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Target Identification with Improved 2D-VMD for Carrier-Free UWB Radar.

Sensors (Basel, Switzerland)·2021
Same author

An Improved RD Algorithm for Maneuvering Bistatic Forward-Looking SAR Imaging with a Fixed Transmitter.

Sensors (Basel, Switzerland)·2017
See all related articles

Related Experiment Video

Updated: Nov 21, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

665

Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning.

Kuiyu Chen1, Shuning Zhang1, Lingzhi Zhu1

  • 1School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Xiao Ling Wei200#, Nanjing 210094, China.

Sensors (Basel, Switzerland)
|January 13, 2021
PubMed
Summary

This study introduces a novel method for radar signal modulation recognition using adaptive singular value reconstruction (ASVR) and deep residual learning. The approach achieves high accuracy even at low signal-to-noise ratios (SNRs), enhancing electronic intelligence capabilities.

Keywords:
adaptive singular value reconstructiondeep residual learningmodulation recognitionradar signals

More Related Videos

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.7K
Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

8.0K

Related Experiment Videos

Last Updated: Nov 21, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

665
Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.7K
Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

8.0K

Area of Science:

  • Electronic intelligence systems
  • Signal processing
  • Machine learning

Background:

  • Automatic modulation recognition is crucial for radar survival in electronic intelligence.
  • Traditional methods struggle with feature extraction at low signal-to-noise ratios (SNRs).

Purpose of the Study:

  • To develop an intelligent radar signal modulation recognition method.
  • To overcome challenges of low SNRs and complex feature extraction.

Main Methods:

  • Utilized adaptive singular value reconstruction (ASVR) for denoising radar signals.
  • Employed image processing techniques on time-frequency distribution images (TFDIs).
  • Applied deep residual learning for modulation classification.

Main Results:

  • ASVR improved time-frequency spectrums of radar signals under low SNRs.
  • Image processing suppressed background noise in TFDIs.
  • The deep residual network achieved 94.1% recognition accuracy for eight modulation types at -8 dB SNR.

Conclusions:

  • The proposed method demonstrates superior robustness and performance in radar signal modulation recognition.
  • This technique is effective even in challenging low SNR environments.
  • It offers an intelligent and efficient alternative to traditional feature extraction methods.