一种基于贝叶斯正交匹配追踪的水下多径稀疏信道估计方法

使用训练序列构成的测量矩阵并采用稀疏恢复算法是近年来常用的多径稀疏信道估计思路。提出一种贝叶斯匹配追踪算法的正交化改进方法,有效地改善了原方法的收敛速度,并将其应用于水下多径稀疏信道估计。进行了新方法的理论推导和两种水下稀疏信道模型中的仿真试验,进而与传统贪婪迭代和贝叶斯估计方法的估计效果进行了对比。仿真结果证明,所提出的新方法比原方法的收敛速度更快,能更高效地进行多径稀疏信道估计。新方法在低信噪比和呈簇状集中分布的水下多径稀疏信道中也有更好的估计效果。

Underwater multipath sparse channel estimation via bayesian orthogonal matching pursuit

Constructing a measuring matrix with training sequence and then using sparse recovery algorithms is a usual approach to multipath sparse channel estimation. In this paper, an improved Bayesian matching pursuit is proposed and applied to underwater multipath sparse channel estimation. We illustrate the method theoretically and test it on two models of underwater multipath sparse channel. Performance of this algorithm is shown in comparison with conven-tional estimating methods. Numerical simulations demonstrate that estimated result of this method converges faster than that of BMP, thus it estimates multipath sparse channel more efficiently. What''s more, the proposed method provides better performance than conventional ones in low-SNR conditions and in the channels with many close paths.