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 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.