AAV Parameters Estimation Based on Improved Time-Frequency Ridge Extraction and Hough Transform
The micro-Doppler effect caused by the rotation of autonomous aerial vehicle (AAV) rotors plays a crucial role in AAV detection and identification, as it can reflect the micro-movement characteristics of the target, enabling the estimation of the blade length and rotation speed. However, existing me...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11091279/ |
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| Summary: | The micro-Doppler effect caused by the rotation of autonomous aerial vehicle (AAV) rotors plays a crucial role in AAV detection and identification, as it can reflect the micro-movement characteristics of the target, enabling the estimation of the blade length and rotation speed. However, existing methods are prone to noise interference and exhibit poor performance in extracting multi-rotor and multi-component signals. In this paper, we first construct a AAV rotor echo model for frequency-modulated radar systems and derive the mapping relationship between rotor parameters and micro-Doppler characteristic components. First-Order short-time Fourier transform synchrosqueezed transform (FSST) is proposed for extracting micro-Doppler features. Specifically, a novel AAV parameter estimation method is investigated, which is based on an improved time-frequency ridge extraction and Hough transform, following a detailed analysis of the micro-Doppler time-frequency spectrum. Finally, the effectiveness of the method is validated through experimental data. Compared to traditional methods, this approach improves the accuracy of multi-rotor, multi micro-Doppler signal parameter estimation. |
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| ISSN: | 2169-3536 |