基于改进稀疏贝叶斯学习的时域信道参数估计方法  

A time domain channel parameters estimation method based on improved sparse Bayesian learning

作　　者：谈玉凡 冯海泓[1] 李记龙[1] 梁金鑫 翟玉爽 TAN Yufan;FENG Haihong;LI Jilong;LIANG Jinxin;ZHAI Yushuang(Shanghai Acoustics Laboratory,Chinese Academy of Sciences,Shanghai 201815,China;University of Chinese Academy of Sciences,Beijing 100049,China;Institute of Acoustic,Chinese Academy of Sciences,Beijing 100049,China;Jiaxing Acousnet Technology Corporation Limited,Jiaxing 314006,Zhejiang,China)

机构地区：[1]中国科学院声学研究所东海研究站,上海201815 [2]中国科学院大学,北京100049 [3]中国科学院声学研究所,北京100049 [4]嘉兴中科声学科技有限公司,浙江嘉兴314006

出　　处：《声学技术》2024年第6期896-906,共11页Technical Acoustics

基　　金：崂山实验室科技创新项目(LSKJ202205105);重点实验室基金项目(2022-JCJQ-LB-033-07)。

摘　　要：针对稀疏贝叶斯学习的信道估计方法在估计信道时延-多普勒二维参数时字典规模过大的问题,文章提出了一种改进的稀疏贝叶斯学习信道估计方法。首先采用匹配滤波器得到接收信号的模糊度函数;然后设置阈值并筛选大于阈值的信道响应,保留对应的索引和原子;接着利用保留的原子构建新的字典矩阵,同时构建相同规模的超参数矩阵;最后将保留的原子逐个代入稀疏贝叶斯学习算法进行超参数的迭代和收敛,最终得到信道冲激响应。该方法在不需要提前获知信道稀疏度的前提下,提高了信道参数估计的分辨率,相比传统的稀疏贝叶斯学习算法,大幅减小了计算量,使得采用稀疏贝叶斯学习算法估计二维时延-多普勒参数成为了可能。仿真结果表明,在预处理阈值设置合理的情况下,该方法相比原始稀疏贝叶斯学习算法,信道估计均方误差至少降低了约一个数量级,相比传统算法一次运算的时间大大缩短。To address the issue that the dictionary matrix is too large in sparse Bayesian learning channel estimation for estimating two-dimensional delay-Doppler parameters,an improved method is proposed in this paper.Firstly,the ambiguity function of the received signal is obtained by using matching filter.Secondly,a threshold is set to retain larger value channel responses,corresponding index values and atoms.Then,a new dictionary matrix and a hyperparameter matrix are constructed at same scale using these retained atoms.At last,the retained atoms are substituted into sparse Bayesian learning algorithm one by one to iterate and converge the hyper-parameters,and the channel impulse response is finally obtained.This method enhances parameter estimation resolution without requiring prior knowledge of channel sparsity.Compared to traditional sparse Bayesian learning,it significantly reduces calculation quantity and enables estimating two-dimensional time-delay Doppler parameters.Simulation results demonstrate that the mean squared error(MSE)estimated with this improved method is at least one order of magnitude lower than that with original sparse Bayesian learning algorithm,while the run time of new method is reduced significantly.

关 键 词：时延-多普勒参数估计 稀疏贝叶斯学习 压缩感知 模糊度函数 

分 类 号：TN911.23[电子电信—通信与信息系统]