基于高斯加权分数阶傅里叶变换的LFM信号参数估计

针对低占空比下传统算法参数估计性能下降的问题,提出了一种高斯加权分数阶傅里叶变换(GFRFT,Gaussian-weighted fractional Fourier transform)参数估计方法。给出了时限信号GFRFT的定义并推导了其模值平方的特性,研究了高斯白噪声背景下GFRFT的输出信噪比并给出了闭式表达式,进行了仿真实验并讨论说明了该方法的适用条件。仿真结果表明,该方法在低占空比的情况下可以有效地提高参数估计精度。

Parameters estimation of LFM signal based on Gaussian-weighted fractional Fourier transform

To overcome the performance degradation of conventional methods in low duty ratio condition, a novel method of parameters estimation for LFM signal based on the Gaussian-weighted fractional Fourier transform (GFRFT) was proposed. Firstly, the GFRFT definition was given and the LFM signal GFRFT with finite duration was derived. Secondly, the statistical characteristics of the GFRFT for the LFM signal under the Gaussian white noise were studied, and a closed mathematical expression of output signal-to-noise ratio was derived. Finally, simulation experiments are conducted, and the applicable condition of the GFRFT is also discussed, which demonstrates that the proposed method can effectively improve parameters estimation performance, especially in the low duty ratio condition.