Estimation method of micro-motion parameters for rotor targets under flashing

Inverse Radon transform is often used to estimate the parameters of micro-motion signals because of its high precision and good denoising performance. However, when the micro-motion signal of the rotor target has a flashing phenomenon, the method fails. In order to solve this problem, a method to estimate the micro-motion parameters under the flashing phenomenon is proposed. Firstly, the scattering point model of single-rotor helicopter radar echoes based on linear frequency modulation signals is established, and the micro-motion characteristics of the echoes under the flashing phenomenon are analyzed. Secondly, the denoising network and the deflashing network are trained respectively through the denosing convolutional neural network(DnCNN) structure to eliminate the noise, flashing band and zero band in the time-frequency diagram of rotor target echoes, and the time-frequency diagram of micro-motion signals enhanced by cosine envelope feature is obtained. Finally, the traditional inverse Radon transform uses the ergodic method to search for micro-motion parameters, which has a large amount of calculation. Therefore, the golden section method is adopted to improve the search process and the speed of parameter estimation, and finally complete the estimation of micro-motion parameters of the rotor target. Simulation results verify the feasibility and effectiveness of the proposed method. © 2022, Editorial Office of Systems Engineering and Electronics. All right reserved.