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

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

2.2K
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
2.2K
Doppler Effect - II01:05

Doppler Effect - II

5.0K
The Doppler effect has several practical, real-world applications. For instance, meteorologists use Doppler radars to interpret weather events based on the Doppler effect. Typically, a transmitter emits radio waves at a specific frequency toward the sky from a weather station. The radio waves bounce off the clouds and precipitation and travel back to the weather station. The radio frequency of the waves reflected back to the station appears to decrease if the clouds or precipitation are moving...
5.0K
NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences01:17

NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences

1.9K
A pulse is a short burst of radio waves distributed over a range of frequencies that simultaneously excites all the nuclei in the sample. Upon passing a radio frequency pulse along the x-axis, the nuclei absorb energy corresponding to their Larmor frequencies and achieve resonance. This shifts the net magnetization vector from the z-axis toward the transverse plane. This angle of rotation of the magnetization vector, or the flip angle, is proportional to the duration and intensity of the pulse.
1.9K
Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

1.4K
The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This...
1.4K
Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

462
Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
462

You might also read

Related Articles

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

Sort by
Same author

A Stage-Aware Cascaded Detection-Segmentation Framework for Leaf Phenotyping and Leaf Dry Biomass Estimation of Pepper Seedlings.

Plants (Basel, Switzerland)·2026
Same author

Nutrient source, temperature, and wetness duration influence fungal growth and conidial germination of <i>Coniella vitis</i>, the main causal agent of grape white rot in China.

Plant disease·2026
Same author

Spatial metabolomic profiling of ripening pepper fruit (Capsicum annuum L.) by MALDI-MSI: Decoding metabolic heterogeneity and its role in quality attribute regulation.

Food chemistry·2026
Same author

Research on Tomato Quality Prediction Models Based on the Coupling of Environmental Factors and Appearance Phenotypes.

Plants (Basel, Switzerland)·2025
Same author

A review of transcriptional control and adaptive functions in terpenoid biosynthesis: Focus on MYB regulatory networks.

Plant science : an international journal of experimental plant biology·2025
Same author

An <i>Aeromonas</i> variant that produces aerolysin promotes susceptibility to ulcerative colitis.

Science (New York, N.Y.)·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Mar 13, 2026

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

8.2K

LPI Radar Waveform Recognition Based on Time-Frequency Distribution.

Ming Zhang1, Lutao Liu2, Ming Diao3

  • 1College of Information and Telecommunication, Harbin Engineering University, Harbin 150001, China. zhangming@hrbeu.edu.cn.

Sensors (Basel, Switzerland)
|October 19, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces an automatic radar waveform recognition system for high noise environments. The system achieves 94.7% accuracy in classifying eight radar signal types, even at a -2 dB signal-to-noise ratio.

Keywords:
LPI radardigital image processingtime-frequency distributionwaveform recognition

More Related Videos

The Frequency Domain Thermoreflectance Technique for Thermal Property Measurements
09:10

The Frequency Domain Thermoreflectance Technique for Thermal Property Measurements

Published on: December 5, 2025

1.0K
A Multimodal Wide-Field Fourier-Transform Raman Microscope
06:48

A Multimodal Wide-Field Fourier-Transform Raman Microscope

Published on: December 30, 2025

656

Related Experiment Videos

Last Updated: Mar 13, 2026

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

8.2K
The Frequency Domain Thermoreflectance Technique for Thermal Property Measurements
09:10

The Frequency Domain Thermoreflectance Technique for Thermal Property Measurements

Published on: December 5, 2025

1.0K
A Multimodal Wide-Field Fourier-Transform Raman Microscope
06:48

A Multimodal Wide-Field Fourier-Transform Raman Microscope

Published on: December 30, 2025

656

Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Signal waveform recognition is crucial for cognitive radio, spectrum management, and radar systems.
  • Low Probability of Intercept (LPI) radar detection necessitates robust signal classification methods.
  • Existing methods may struggle in high noise environments, impacting performance.

Purpose of the Study:

  • To propose an automatic radar waveform recognition system capable of operating effectively in high noise conditions.
  • To classify eight distinct radar signal types, including linear frequency modulation (LFM) and various coded modulations.
  • To leverage advanced feature extraction and selection techniques for improved classification accuracy.

Main Methods:

  • Utilized an Elman neural network (ENN) as the supervised classifier.
  • Extracted features from Choi-Williams time-frequency distribution (CWD) images using techniques like image filtering, skeleton extraction, and Pseudo-Zernike moments.
  • Applied principal component analysis (PCA) and mutual information for feature selection to reduce redundancy and simplify calculations.

Main Results:

  • The proposed system demonstrated high performance in classifying eight types of radar signals.
  • Achieved an overall successful recognition ratio (RSR) of 94.7% at a low signal-to-noise ratio (SNR) of -2 dB.
  • The feature selection algorithm effectively reduced computational complexity while maintaining high accuracy.

Conclusions:

  • The developed automatic radar waveform recognition system is effective in high noise environments.
  • The combination of CWD-based feature extraction and ENN classification provides a robust solution for LPI radar signal identification.
  • The system shows significant promise for applications in advanced radar and electronic warfare systems.