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Discrete Fourier Transform

Discrete Fourier Transform

The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...

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Updated: Oct 13, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

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

Huiling Hou¹, Zhiliang Yang¹, Cunsuo Pang¹

¹National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan 030051, China.

Sensors (Basel, Switzerland)

|November 13, 2021

Summary

A new Fractional Fourier Transform (FRFT)-based method enhances micro-Doppler signal analysis for Unmanned Aerial Vehicle (UAV) detection and rotor parameter estimation, improving accuracy over standard techniques.

Keywords:

FRFT FSST parameter estimation rotor UAV

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Last Updated: Oct 13, 2025

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Published on: October 28, 2022

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Published on: April 26, 2014

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.