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基于分数阶傅里叶变换-分数阶短时停时变换的旋翼无人机微动多普勒信号检测与参数估计

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

作者信息

Hou Huiling, Yang Zhiliang, Pang Cunsuo

机构信息

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

出版信息

Sensors (Basel). 2021 Nov 3;21(21):7314. doi: 10.3390/s21217314.

DOI:10.3390/s21217314
PMID:34770622
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8587711/
Abstract

The micro-Doppler signal generated by the rotors of an Unmanned Aerial Vehicle (UAV) contains the structural features and motion information of the target, which can be used for detection and classification of the target, however, the standard STFT has the problems such as the lower time-frequency resolution and larger error in rotor parameter estimation, an FRFT (Fractional Fourier Transform)-FSST (STFT based synchrosqueezing)-based method for micro-Doppler signal detection and parameter estimation is proposed in this paper. Firstly, the FRFT is used in the proposed method to eliminate the influence of the velocity and acceleration of the target on the time-frequency features of the echo signal from the rotors. Secondly, the higher time-frequency resolution of FSST is used to extract the time-frequency features of micro-Doppler signals. Moreover, the specific solution methodologies for the selection of window length in STFT and the estimation of rotor parameters are given in the proposed method. Finally, the effectiveness and accuracy of the proposed method for target detection and rotor parameter estimation are verified through simulation and measured data.

摘要

无人机（UAV）旋翼产生的微多普勒信号包含目标的结构特征和运动信息，可用于目标检测与分类。然而，标准的短时傅里叶变换（STFT）存在时频分辨率较低以及旋翼参数估计误差较大等问题。本文提出了一种基于分数阶傅里叶变换（FRFT）-同步挤压短时傅里叶变换（FSST）的微多普勒信号检测与参数估计方法。首先，该方法利用FRFT消除目标速度和加速度对旋翼回波信号时频特征的影响。其次，利用FSST较高的时频分辨率提取微多普勒信号的时频特征。此外，该方法还给出了STFT中窗长选择和旋翼参数估计的具体求解方法。最后，通过仿真和实测数据验证了该方法在目标检测和旋翼参数估计方面的有效性和准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/69db29e20876/sensors-21-07314-g010a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/aa2660338af4/sensors-21-07314-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/02f94b1a9c05/sensors-21-07314-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/9ac15c65ffcf/sensors-21-07314-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/13af1fb5eed3/sensors-21-07314-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/cdf72145d3d4/sensors-21-07314-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/fdd0de5d39f4/sensors-21-07314-g006a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/8e9e5f89d0cd/sensors-21-07314-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/c216e142e173/sensors-21-07314-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/3b4b7e192659/sensors-21-07314-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/69db29e20876/sensors-21-07314-g010a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/aa2660338af4/sensors-21-07314-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/02f94b1a9c05/sensors-21-07314-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/9ac15c65ffcf/sensors-21-07314-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/13af1fb5eed3/sensors-21-07314-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/cdf72145d3d4/sensors-21-07314-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/fdd0de5d39f4/sensors-21-07314-g006a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/8e9e5f89d0cd/sensors-21-07314-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/c216e142e173/sensors-21-07314-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/3b4b7e192659/sensors-21-07314-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc27/8587711/69db29e20876/sensors-21-07314-g010a.jpg

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本文引用的文献

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Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review.多旋翼无人机在雷达传感器网络中的检测与分类：综述。
Sensors (Basel). 2020 Jul 27;20(15):4172. doi: 10.3390/s20154172.
3
Micro-Doppler Signal Time-Frequency Algorithm Based on STFRFT.基于分段短时间分数阶傅里叶变换的微多普勒信号时频算法
Sensors (Basel). 2016 Sep 24;16(10):1559. doi: 10.3390/s16101559.