采用图像处理的跳频信号参数盲估计

针对短波复杂信道环境下的跳频信号参数估计问题，提出了一种基于图像处理的跳频信号参数盲估计算法。该算法在时频分析的基础上采用灰度共生矩阵提取信号的纹理特征，通过对纹理特征量的分割实现信号与背景噪声的分割，并运用形态学滤波去除二值化后产生的椒盐噪声；然后根据连通区域标记得到的各个信号在时频图中的位置信息来聚类，从而去除定频、突发等干扰信号，分选出跳频信号；最后根据分选出的跳频信号提取其跳频频线并进行修正，估计出跳频信号的跳周期、跳变时刻和跳频频率。仿真实验表明，该算法切实有效，能够在较低的信噪比条件下精确地估计出跳频信号的参数。

Blind parameter estimation of frequency hopping signals by image processing

In order to estimate the parameters of the frequency hopping(FH) signals in complex high frequency environment,a blind parameter estimation method based on texture feature is proposed.In the algorithm the received signals are represented as time-frequency diagram,and texture features are extracted from the time-frequency diagram by using gray level co-occurrence matrix(GLCM). Then the background noise can be removed through the separation of texture features,and the salt-and-pepper noise after binarization is eliminated by morphological filtering.Then this algorithm labels all the connected components in the time-frequency diagram to get their location information,and removes the frequency-fixed and burst interference by means of clustering,so the FH signals can be sorted out. After that the edge information is abstracted and modified to estimate the parameters such as hopping rate,hopping time and hopping frequency. Simulation results show that the algorithm can separate the background noise from the signals more effectively,and can estimate the parameters accurately even when the signal-to-noise ratio(SNR) is low.