Micro-Doppler Frequency Estimation Based on Radon-Wigner Transform

Sun, Huixia; Shidong, Su

doi:10.14429/dsj.64.2980

Cited by 2 publications

(2 citation statements)

References 7 publications

(3 reference statements)

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“…Then the envelope walk compensation and phase compensation of the time-frequency signal is completed. However, traditional time-frequency analysis methods, such as shorttime Fourier transform (STFT) [14], Wigner-Ville Distribution (WVD) [15], Radon-Wigner Transform (RWT) [16], Wigner-Hough Transform (WHT) [17], etc., not only require a high SNR, but also is limited by many factors, including the resolution of the time-frequency distribution, the accuracy of peak extraction and the accuracy of polynomial fitting, etc.. In addition, when detecting multiple targets, there exist cross components which will conceal the signal of weak targets [18].…”

Section: Introductionmentioning

confidence: 99%

Detection and Identification of Low-Slow-Small Rotor Unmanned Aerial Vehicle Using Micro-Doppler Information

Song

Huo

2021

IEEE Access

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The capability of a radar system to detect and identify the low-slow-small rotor unmanned aerial vehicle (UAV) is intensely important for management and control in low altitude, and it can be enhanced by the characteristics of UAV, which inherently carries micro-Doppler information. In this paper, we establish a micro-motion model of rotor UAV, and analyze the range walking and Doppler broadening caused by micro-motion. Hence, based on the characteristics of micro-Doppler of the rotors apart from the main body for the moving UAV, a micro-motion target detection and micro-Doppler parameters estimation method is proposed. Firstly, a method based on datashift in range dimension is used to compensate the translational component of the main body. Secondly, for the echo after translation compensation, a parameter estimation method of micro-Doppler based on the optimal demodulation operator is proposed. The operator contents the maximum likelihood criterion, which strengthens the difference between the micro-Doppler signal and the background clutters, so that more signal energy can be accumulated while the clutter is suppressed. Subsequently, the micro-Doppler parameters of the micro-motion component are obtained by the optimal demodulator, so that the detection and identification of the target are realized. Additionally, the performance of proposed method, including parameter estimation, signal-to-noise ratio (SNR) improvement and the calculation efficiency is analyzed. Finally, the simulation and experimental results reveal that the proposed method does possess the capability to improve the performance of detection and recognition of rotor UAV.INDEX TERMS Micro-Doppler, unmanned aerial vehicle (UAV) , optimal demodulation operator, micromotion parameter estimation, micro-motion target detection and identification

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Section: Introductionmentioning

confidence: 99%

Detection and Identification of Low-Slow-Small Rotor Unmanned Aerial Vehicle Using Micro-Doppler Information

Song

Huo

2021

IEEE Access

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“…The Doppler frequency shift reflects the velocity of the moving target. Target's micro-motions such as rotation, vibration may induce additional Doppler frequency modulations which are called the micro-Doppler effects [8][9][10][11][12][13][14][15] .…”

Section: Introductionmentioning

confidence: 99%

Template free Micro Doppler Signature Classification for Wheeled and Tracked Vehicles

et al. 2019

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The micro-Doppler signature is a time-varying frequency modulation imparted on radar echo caused by target's micro-motion. To save the trouble of constructing template in the target classification, this paper investigates the micro-Doppler signature of wheeled and tracked vehicles and proposes a template-free classification method. Firstly, the echo signature is established and the micro-Doppler difference of these two kinds of targets is analysed. Secondly, some new micro-Doppler features are defined according to their difference. The new defined features are micro-Doppler bandwidth, micro-Doppler expansion rate and micro-Doppler peak number. According to the characteristic of the micro-Doppler in the time-frequency domain, we proposed to realise the feature extraction by Hough transformation. Lastly, template-free subjection functions are proposed to define the relationship between the features and the vehicles. By fuzzy comprehensive evaluation, the final classification result is obtained by combining the subjection probabilities together. Experimental results based on the simulated data and measured data are presented, which prove that the algorithm has good performance.

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Micro-Doppler Frequency Estimation Based on Radon-Wigner Transform

Cited by 2 publications

References 7 publications

Detection and Identification of Low-Slow-Small Rotor Unmanned Aerial Vehicle Using Micro-Doppler Information

Detection and Identification of Low-Slow-Small Rotor Unmanned Aerial Vehicle Using Micro-Doppler Information

Template free Micro Doppler Signature Classification for Wheeled and Tracked Vehicles

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