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.
Highlights
In recent years, the incidents such as Unmanned Aerial Vehicle (UAV) crashing, injuring people, or damaging properties, etc. that civil UAVs endanger public safety have frequently occurred, for which the implementation of safety supervision and prevention has been a “new normal” in [1,2].Since most of the civil UAVs have rotors, the micro-Doppler signals produced contain information such as the structural features and motion status of the target, which can be used to improve the accuracy of the classification and recognition of the target in [3,4]
In [8], the micro-Doppler effect caused by small UAVs was studied, and the effectiveness of the micro-Doppler signal for target detection was verified by using simulation and measurement data
In [12], a time-frequency analysis method for micro-Doppler signal based on STFRFT (Short-Time Fractional Fourier Transform) was studied, significantly improving the time-frequency resolution compared with STFT
Summary
The incidents such as UAV crashing, injuring people, or damaging properties, etc. that civil UAVs endanger public safety have frequently occurred, for which the implementation of safety supervision and prevention has been a “new normal” in [1,2]. In [28], it was pointed out that the micro-Doppler effect was an important feature for identifying small UAVs, and it was further proved that SST had a higher time-frequency resolution with measured data. An echo model of the rotor UAV is given in the Section 2 of this paper; a proposed algorithm is given in the Section 3; a method for the parameter estimation of the proposed algorithm is indicated in the Section 4; the influence of the rotor parameters on the focus performance of micro-Doppler signal is analyzed in the Section 5; the effectiveness of the algorithm is verified by using simulation and measured data in the Section 6.
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