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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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 C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...

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

Yuying Zhu1, Shuning Zhang1, Huichang Zhao1

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

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary

Carrier-free ultra-wideband (UWB) radar enhances radar automatic target recognition (RATR) by providing detailed object information. New methods improve noise reduction and feature extraction for accurate target classification, even in low signal-to-noise ratio environments.

Keywords:
2D-IVMDDCNN multi-views signalscarrier-free ultra-wideband radartransfer learning

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Area of Science:

  • Radar Systems Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Interest in carrier-free ultra-wideband (UWB) radar for radar automatic target recognition (RATR) is growing.
  • UWB radar offers more detailed target information compared to narrow-band systems.
  • Real-world environments present challenges due to noise and clutter.

Purpose of the Study:

  • To develop and validate a robust RATR system using carrier-free UWB radar.
  • To enhance noise reduction and feature extraction techniques for improved target recognition.
  • To achieve reliable classification performance in low signal-to-noise ratio (SNR) conditions.

Main Methods:

  • Accurate geometric models acquired using 3ds Max.
  • Echo signal acquisition via time-domain integral equation (TDIE) for short-duration UWB signals.
  • Proposed improved two-dimensional variational mode decomposition (2D-IVMD) for noise elimination and preliminary edge feature extraction.
  • Deep conventional neural network (DCNN) for final target recognition.

Main Results:

  • Electromagnetic modeling accuracy verified by comparing simulated and actual waveforms.
  • 2D-IVMD effectively reduces noise and extracts crucial edge features.
  • DCNN achieves promising classification performance.
  • The integrated approach demonstrates effectiveness in low SNR environments.

Conclusions:

  • The proposed methodology, combining UWB radar, advanced signal processing (2D-IVMD), and deep learning (DCNN), provides a robust solution for RATR.
  • The system demonstrates significant potential for accurate target recognition even under challenging noisy conditions.
  • This research lays the groundwork for more sophisticated feature extraction and recognition algorithms in UWB radar systems.