稳健的多跳频信号跟踪方法

Robust tracking method for multiple frequency-hopping signals

首先,采用t分布取代高斯分布来描述噪声,对信号的稀疏贝叶斯模型进行修正,减弱模型对脉冲噪声异常值的敏感性。然后,推导出一种多向量变分稀疏贝叶斯学习算法来求解修正模型,利用多个阵元的数据重构信号稀疏系数,提高了重构精度、改善了方法的跟踪性能。最后,设计试验对跟踪方法的性能进行仿真验证。试验结果表明,该方法在脉冲噪声下具有良好的稳健性和精确性。

A robust tracking method for multiple frequency-hopping signals is presented.First,the measurement noise is modeled using the t distribution.Then,a novel Bayesian probability model is proposed to describe the multiple Frequency-Hopping（FH）signals tracking problem,such that the influence of the impulsive noise on the tracking performance can be attenuated efficiently.Finally,a Multiple Variational Sparse Bayesian Learning（M-VSBL）algorithm is derived for the proposed Bayesian model to estimate the hopping frequencies,directions of arrival and detect the hopping times.Simulation results demonstrate the robustness and accuracy of the proposed method under impulsive noise.