闪烁现象下旋翼目标微动参数估计方法

逆Radon变换以其精度高、抗噪性能好的优点常用于微动信号的参数估计,但是当旋翼类目标微动信号存在闪烁现象时,该方法失效.针对此问题,提出一种闪烁现象下的微动参数估计方法.首先,建立基于线性调频信号的单旋翼直升机雷达回波散射点模型,分析闪烁现象下回波的微动特性.其次,通过去噪卷积神经网络(denosing convol...

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.