【摘要】 针对传统非线性时频分析方法在跳频(frequency hopping,FH)信号参数估计时,会出现严重的交叉项和参数估计精度降低等问题,引入径向高斯核(radially Gaussian kernel,RGK)时频分析方法,该方法根据FH信号的不同自适应选择最优核函数,从而有效抑制交叉项。RGK时频分析方法可在高斯噪声环境下估计FH信号的参数,但在脉冲性较强的α稳定分布噪声中,该方法性能退化甚至失效。对此,结合最大似然估计理论,提出了一种α稳定分布噪声环境下的加权最大似然广义柯西(weighted maximum-likelihood generalized Cauchy,WMGC)滤波的新方法。采用基于WMGC滤波器的RGK时频分析方法(WMGC-RGK方法,即WR方法),对该噪声中的跳频信号进行参数估计。仿真结果表明,与基于分数低阶及Myriad的时频分析方法相比,WR方法在α稳定分布噪声中具有良好的鲁棒性和优良的跳频信号参数估计性能。更多还原

【Abstract】 In view of the problem that conventional non-linear time-frequency analysis methods in frequency hopping(FH)signals parameter estimation suffer from the effect of serious cross-components,a radially Gaussian kernel(RGK)time-frequency analysis method is introduced.To suppress the cross-components,it selects the adaptive optimal kernel depending on a variety of signals.The RGK time-frequency analysis method can estimate FH signals parameters in Gaussian noise,but its performance in heavy-tailed impulsive noise environment falls into severe degradation.Combined with the maximum likelihood estimation theory,a weighted maximumlikelihood generalized Cauchy(WMGC)method for the case ofαstable distribution noise is proposed.The parameters of noisy FH signals can be estimated by the RGK time-frequency analysis method based on the WMGC filter(WMGC-RGK method,simply WR method).Simulation results show that compared with the fractional lower order statistics as well as the Myriad filter based time frequency analysis methods,the proposed method has better performance on the FH signals parameter estimation and it is robust to theαstable distribution noise.更多还原