利用稀疏贝叶斯理论的跳时估计方法

当跳频信号的频率不在预设的频率集中时,为了提高跳时估计的正确率,提出了一种基于稀疏贝叶斯理论的跳时估计方法。该方法首先在信号模型中设置频率偏差参数;其次利用狄利克雷过程以及稀疏贝叶斯理论,设计接收信号模型中各个参数的迭代规则,并在每次迭代中利用频率偏差参数修正频率字典矩阵;最后,算法收敛时可得到用于计算谱图的稀疏矩阵,进而可以得到跳时的估计值。仿真结果表明,该算法估计的跳时正确率高于其他方法,并且计算的谱图的真实性也高于其他方法。

Hop timing estimation method by exploiting sparse Bayesian inference

In order to improve the performance of hop timing estimation when the frequencies of frequency hopping (FH) signals are not in the known frequency set, a novel method is developed based on the sparse Bayesian inference (SBI). The proposed method first sets the frequency bias in the signal model. The updating rules of the parameters are calculated based on the Dirichlet process and SBI, and then the frequency bias is utilized to correct the dictionary matrix. Finally, the sparse matrices and the hop timing estimation can be obtained. Simulation results demonstrate that compared to the existing methods, the proposed method can obtain higher correct ratios of the hop timing estimation and a better performance of the spectrum estimation.