| 摘要: |
| 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。 |
| 关键词: 跳频信号 参数估计 时频分析 自相关运算 |
| DOI：10.20079/j.issn.1001-893x.220606003 |
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| 基金项目: |
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| Frequency Hopping Parameter Estimation Based on Autocorrelation and Time-Frequency Analysis |
| ZHANG Wei,WANG Ping,XIE Xikun |
| ((1.School of Electronics Engineering,Naval University of Engineering,Wuhan 430033,China;2.Unit 92038 of PLA,Qingdao 266041,China;3.Unit 91202 of PLA,Huludao 125000,China)) |
| Abstract: |
| 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. |
| Key words: frequency hopping signal parameter estimation time-frequency analysis autocorrelation operation |
