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Theoretical Modeling and Experimental Study on Low-Altitude Slow-Small Target (LSS) Detection Based on Broadband

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This study introduces broadband spectral modulation imaging (BSMI) for detecting small, low-altitude drones. The novel BSMI system achieves over 90% classification accuracy, enhancing drone detection and reducing false alarms.

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Area of Science:

  • Radar Systems and Signal Processing
  • Remote Sensing Technology
  • Aerospace Engineering

Background:

  • Low-altitude, slow-small (LSS) targets like drones present detection challenges due to small radar cross-section (RCS) and low signal-to-clutter ratio (SCR).
  • Conventional radar and electro-optical methods have limitations in effective range and are susceptible to background clutter in complex environments.

Purpose of the Study:

  • To propose and validate a novel detection theory, broadband spectral modulation imaging (BSMI), for enhanced detection of LSS targets.
  • To overcome the limitations of existing detection technologies for drones.

Main Methods:

  • Analysis of drone target recognition accuracy using numerical simulations across various zenith angles and detection ranges.
  • Design and implementation of a snapshot-based BSMI detection system.
  • Experimental validation of the BSMI system under controlled conditions.

Main Results:

  • Numerical simulations analyzed drone recognition accuracy.
  • The implemented BSMI system demonstrated over 90% classification accuracy in experimental validation.
  • The BSMI approach confirmed its effectiveness in enhancing detection probability.

Conclusions:

  • The proposed BSMI theory and system offer a viable technical solution for monitoring unauthorized aerial activities.
  • The study significantly improves detection probability and suppresses false alarms for low-altitude drones.
  • BSMI technology provides a robust method for addressing the challenges of LSS target detection.