Rotor UAV's Micro-Doppler Signal Detection and Parameter Estimation Based on FRFT-FSST.

Area of Science:

• Radar signal processing
• Aerospace engineering
• Signal analysis

Background:

• Micro-Doppler signals from Unmanned Aerial Vehicle (UAV) rotors contain vital target information.
• Standard Short-Time Fourier Transform (STFT) methods suffer from low time-frequency resolution and rotor parameter estimation errors.

Purpose of the Study:

• To propose an improved method for micro-Doppler signal detection and parameter estimation using FRFT and FSST.
• To address the limitations of standard STFT in UAV rotor analysis.

Main Methods:

• Utilizing Fractional Fourier Transform (FRFT) to mitigate target velocity and acceleration effects on echo signals.
• Employing STFT-based synchrosqueezing (FSST) for enhanced time-frequency resolution and feature extraction.
• Developing specific methodologies for STFT window length selection and rotor parameter estimation.

Main Results:

• The proposed FRFT-FSST method effectively detects UAVs and estimates rotor parameters.
• Demonstrated higher accuracy in parameter estimation compared to conventional methods.
• Validated through both simulation and real-world measured data.

Conclusions:

• The FRFT-FSST approach offers a significant advancement in UAV micro-Doppler signal processing.
• This method provides a more accurate and reliable means for UAV detection and rotor characteristic analysis.