基于自相关和时频分析的跳频参数估计

Frequency Hopping Parameter Estimation Based on Autocorrelation and Time-Frequency Analysis

为解决低信噪比条件下跳频参数估计算法性能低的问题,提出了一种基于自相关和时频分析的跳频参数估计算法。首先,采用基于能量检测的分段自相关算法对接收端信号进行预处理;然后,进行时频变换,得到信号的时频矩阵,通过二值化和形态学滤波完成对信号的降噪提取;最后,通过聚类算法完成参数估计。仿真实验表明,该算法具有较高的估计精度和良好的抗噪声性能,在信噪比最低为-11 dB时估计误差数量级仍为10^(-7),同时自相关运算对参数估计算法的抗噪声性能具有明显的提高作用。

In order to obtain high-definition time-frequency map and high-precision parameter estimation of frequency hopping(FH)signals,an FH signal parameter estimation method based on autocorrelation and time-frequency analysis method is proposed.Firstly,the segmented autocorrelation algorithm based on energy detection is used to preprocess the receiving signal,and then the time-frequency transformation is carried out to obtain the time-frequency matrix of the signal.The signals are extracted by binarization and morphological filtering,and the parameters are estimated by clustering algorithm.Simulation results show that under the condition of low signal-to-noise ratio(SNR),this method can obtain high-definition time-frequency images and high-precision parameter estimates.When the lowest SNR is-11 dB,the order of magnitude of the estimation error is 10^(-7).Meanwhile,the autocorrelation operation can improve the anti-noise performance of the parameter estimation algorithm obviously.