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W-Band Multi-Aspect High Resolution Range Profile Radar Target Classification Using Support Vector Machines.

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Summary
This summary is machine-generated.

This study developed two efficient millimeter-wave radar classifiers for maritime targets, even with limited data. These methods are suitable for resource-constrained systems like unmanned aircraft systems (UASs).

Keywords:
automatic target recognition (ATR)high-resolution range profile (HRRP)millimeter-wave (mmW) imagingradar target classificationsupport vector machine (SVM)

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Area of Science:

  • Radar Systems Engineering
  • Machine Learning for Signal Processing
  • Maritime Surveillance Technology

Background:

  • Millimeter-wave (W-band) radar is crucial for maritime surveillance.
  • Developing classifiers for resource-limited platforms like unmanned aircraft systems (UASs) presents challenges with limited and imbalanced datasets.
  • High-resolution range profiles (HRRPs) offer a reliable and fast method for radar target characterization.

Purpose of the Study:

  • To develop a multiaspect classifier for maritime targets using W-band radar data.
  • To address challenges in resource-limited implementations, focusing on tactical UASs and small datasets.
  • To create simple yet effective Support Vector Machine (SVM)-based classifiers.

Main Methods:

  • Collected W-band radar measurements of maritime targets with Attitude and Heading Reference Systems (AHRSs) in a littoral environment.
  • Processed data using High-Resolution Range Profiles (HRRPs), including range alignment via peak detection and Fast Fourier Transforms (FFTs).
  • Developed two linear SVM classifiers: one using SVM statistics (PPV, NPV) for target probabilities and aspect transitions, and another using Kolmogorov-Smirnov test for dimensionality reduction and feature vector concatenation.

Main Results:

  • Both developed SVM-based classifiers demonstrated excellent performance in their general forms.
  • The first approach effectively used PPV and NPV for optimal aspect transition determination.
  • The second approach, employing the Kolmogorov-Smirnov test, proved suitable for resource-constrained scenarios, enabling field updates.

Conclusions:

  • Simple, linear SVM classifiers utilizing frequency domain HRRPs are effective for maritime target classification.
  • The proposed methods are well-suited for resource-limited platforms like tactical UASs, even with imbalanced datasets.
  • The dimensionality reduction approach offers flexibility for on-field model adaptation and updates.