基于稀疏贝叶斯学习的多跳频信号DOA估计方法

Direction of Arrival Estimation for Multiple Frequency Hopping Signals Based on Sparse Bayesian Learning

针对多跳频信号空域参数估计问题,该文在稀疏贝叶斯学习(SBL)的基础上,利用跳频信号的空域稀疏性实现了波达方向(DOA)的估计。首先构造空域离散网格,将实际DOA与网格点之间的偏移量建模进离散网格中,建立多跳频信号均匀线阵接收数据模型;然后通过SBL理论得到行稀疏信号矩阵的后验概率分布,用超参数控制偏移量和信号矩阵的行稀疏程度;最后利用期望最大化(EM)算法对超参数进行迭代,得到信号矩阵的最大后验估计以完成DOA的估计。理论分析与仿真实验表明该方法具有良好的估计性能并能适应较少快拍数的情况。

To solve the problem of spatial parameter estimation of multi-frequency hopping signals, the sparsity in spatial domain of frequency hopping signals is used to realize the Direction Of Arrival (DOA) estimation based on Sparse Bayesian Learning (SBL). First, the spatial discrete grid is constructed and the offset between the actual DOA and the grid points is modeled into it. The data model of the uniform linear array with multiple frequency hopping signals is established. Then the posterior probability distribution of the sparse signal matrix is obtained by the SBL theory, and the line sparsity of the signal matrix and the offset is controlled by the hyperparameters. Finally, The expectation maximization algorithm is used to iterate the hyper parameters, and the maximum posteriori estimation of the signal matrix is obtained to complete the DOA estimation. Theoretical analysis and simulation experiments show that this method has good estimation performance and can adapt to less snapshots.