Research On Micro-Doppler Feature Extraction Method Of Uav Rotor Based On Wigner-Ville Distribution

In recent years,small UAVs have received more and more attention due to their low price,outstanding mobility,easy handling,and difficulty in detection,bringing many conveniences to people’s lives,but also causing many problems to social security and airspace control,so how to accurately detect UAV targets is an important premise for their control.In latest years,the detection and identification of UAV targets has been extensively studied in the field of radar detection.Because of low-altitude flight,lowvelocity flight and low radar scattering cross-sectional area of UAVs,they are typical "low,slow and small" targets,therefore,the target signal is difficult to be extracted from the complex ground noise background using conventional methods.Since the Doppler spectrum shift caused by the small movements of the target can indicate the message of the target’s form,dimensions,and movement status,and the micro-motion feature is unique,so the detection and identification of "low,slow and small" targets can be effectively solved by exploiting the micro-Doppler features resulting from the micro-motion of the targets.In this thesis,the following aspects of the UAV rotor micro-Doppler feature extraction method are studied in depth.Firstly,the principle of micro-Doppler effect and the modulatory function of object micro-motion for radar signal are analyzed,the mathematical model of rotor blade echo of rotorcraft UAV is derived on the basis of typical micro-motion model,and the simulated signal of UAV rotor blade radar echo is generated.Based on the extraction results of the simulated signal,the micro-Doppler performance characteristics of the UAV rotor blade signal in the time-frequency domain are summarized and the physical scattering principle for the appearance of the characteristics is analyzed.In this thesis,several typical time-frequency analysis methods are introduced and their performance is analyzed by simulation.For the Wigner-Ville distribution,which has good time-frequency characteristics but has the problem of cross-term interference,a spectral rearrangement algorithm is introduced to rearrange the time-frequency spectral energy based on the extracted results of the traditional smooth pseudo-WignerVille distribution.The smooth pseudo-Wigner-Ville distribution combined with the spectral rearrangement algorithm can not only eliminate the cross term interference but also improve the aggregation of the time-frequency spectral energy.In this thesis,a parameter-optimized variational modal decomposition algorithm combined with the micro-Doppler feature extraction method of Wigner-Ville distribution is proposed.The method adopts variational mode decomposition method to resolve the signal to multiple inherent mode part signals,and uses a particulate swarm optimization method for improving its performance;after decomposing the analysis signal,adopt the Wigner-Ville distribution to extract the micro-Doppler features for each modal component separately,and then the final micro-Doppler features are obtained after energy superposition.This method uses the idea of decomposing complex signals into simple signals,and finally achieves the purpose of reducing the influence of crossover items in the Wigner-Ville distribution.