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A bat biomimetic model for scenario recognition using echo Doppler information.

Wang Feng1, Pang Chunyang1, Lu Yuqing1

  • 1Hohai University, Nanjing, Jiangsu 211100, People's Republic of China.

Bioinspiration & Biomimetics
|February 5, 2024
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Summary

Bats use Doppler frequency shifts in echolocation for navigation. This study biomimics bat sonar using a convolutional neural network (CNN) to improve ultrasound sonar systems for enhanced environmental recognition.

Keywords:
Doppler shiftconvolutional neural networkintelligent bionicmulti-scale time-frequency featurescenarios recognition

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Area of Science:

  • Bioacoustics
  • Artificial Intelligence
  • Robotics

Background:

  • Bats utilize Doppler frequency shifts in their echolocation signals to perceive their environment.
  • Existing ultrasound sonar systems can benefit from biomimetic approaches for enhanced environmental recognition during motion.

Purpose of the Study:

  • To develop and validate a biomimetic bat sonar model for improved environmental recognition.
  • To enhance the scene recognition capabilities of human ultrasound sonar systems by incorporating echo Doppler information.

Main Methods:

  • Established a 3D clutter model for bat bio-sonar in a flying state using constant frequency signals.
  • Proposed a scene recognition method combining multi-scale time-frequency analysis with a convolutional neural network (CNN).
  • Extracted Doppler and range dimensions using short-time Fourier transform at different scales, fusing them into multi-scale feature planes.

Main Results:

  • The proposed algorithm achieved over 98% accuracy in varied clutter scenarios at a 30 dB signal-to-noise ratio in simulations.
  • Experimental validation confirmed the effectiveness of Doppler information for classifying and recognizing cluttered environments.
  • The biomimetic approach significantly improved scenario classification and recognition performance.

Conclusions:

  • The study demonstrates the potential of biomimetic bat sonar for advanced environmental recognition.
  • The developed algorithm shows promise for applications in ultrasonic sonar systems, including navigation robots and helicopter obstacle avoidance.
  • Integrating Doppler and range information via CNNs offers a robust solution for real-time motion-based environmental perception.