基于柯西分布的跳频信号参数最大似然估计方法

该文针对传统的跳频信号参数估计方法在alpha稳定分布噪声下性能严重退化的问题，引入基于柯西分布的最大似然估计方法。将跳频信号分解到由信号包络参数和频率参数构成的2维平面，基于柯西分布建立最大似然函数，在抑制alpha稳定分布噪声的同时，直接对信号的频率参数进行估计。在构建的最大似然函数基础上，该方法依据跳频信号的短时平稳性，对信号进行加窗，有效获得信号的跳频频率及其跳变次序，进而实现对信号的跳变时刻和跳频周期等参数的估计。仿真结果表明，在alpha稳定分布噪声环境中，相比基于分数低阶统计量及基于Myriad滤波的时频分析方法，该文所提方法提高了跳频信号的参数估计精度，具有良好的稳健性。

Maximum-likelihood Estimation for Frequency-hopping Parameters by Cauchy Distribution

In view that conventional methods for Frequency Hopping (FH) signal parameter estimation suffer from performance degradation in alpha stable noise environment, the Cauchy based maximum likelihood estimation method is introduced in this paper. The FH signal is decomposed into the two-dimensional envelope versus frequency plane, and then a maximum-likelihood function based on Cauchy distribution is established to extract the frequency parameter directly. For the short-time stationarity of FH signals, the maximum-likelihood function is windowed in order to estimate the specific values and sequence of frequency-hopping, after that the hopping timing and the duration can be estimated. 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 improves the estimation accuracy of FH signal parameters and is robust to the alpha stable distribution noise.