张鹏,,
巴斌
信息工程大学信息系统工程学院 ??郑州 ??450001
基金项目:国家自然科学基金(61401513)
详细信息
作者简介:崔维嘉：男，1976年生，博士，副教授，研究方向为移动通信、信号处理等
张鹏：男，1993年生，硕士生，研究方向为通信信号处理、稀疏重构等
巴斌：男，1987年生，博士，讲师，研究方向为阵列信号处理、参数估计等
通讯作者:张鹏 ieu_zp@outlook.com
中图分类号:TN911.7计量
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被引次数:0
出版历程
收稿日期:2018-12-24
修回日期:2019-04-12
网络出版日期:2019-04-25
刊出日期:2019-10-01
Sparse Reconstruction OFDM Delay Estimation Algorithm Based on Bayesian Automatic Relevance Determination
Weijia CUI,Peng ZHANG,,
Bin BA
Institute of Information System Engineering, The Information Engineering University, Zhengzhou 450001, China
Funds:The National Natural Science Foundation of China (61401513)
摘要
摘要:针对复杂环境下，单测量矢量(SMV)条件下的正交频分复用(OFDM)时延估计问题，该文提出了一种基于贝叶斯自动相关性确定(BARD)的稀疏重构时延估计算法。该算法运用贝叶斯框架，从进一步挖掘有用信息的角度入手，引入不对称的自动相关性确定(ARD)先验，融入参数估计过程中，有效提升了低信噪比(SNR)和SMV条件下的时延估计精度。该算法首先基于OFDM信号物理层协议数据单元估计出的信道频域响应构造稀疏化实数域表示模型，然后对模型中的噪声和稀疏系数矢量进行概率假设，同时引入自动相关性确定先验；最后根据贝叶斯框架，通过期望最大化(EM)算法求解超参数，实现对时延的估计。仿真实验表明，该算法具有更好的估计性能，在信噪比较高时更加贴近克拉美罗界(CRB)。同时基于通用软件无线电外设(USRP)，利用实际信号对所提算法进行了有效性地验证。
关键词:时延估计/
神经网络/
自动相关性确定(ARD)/
通用软件无线电外设(USRP)
Abstract:Considering the problem of Orthogonal Frequency Division Multiplexing (OFDM) signal delay estimation with only a Single Measurement Vector (SMV) in a complex environment, a sparse reconstruction time delay estimation algorithm based on Bayesian Automatic Relevance Determination (BARD) is proposed. The Bayesian framework is used to start from the perspective of further mining useful information, and asymmetric Automatic Relevance Determination(ARD) priori is introduced to integrate into the parameter estimation process, which improves the accuracy of time delay estimation under SMV and low Signal-to-Noise Ratio (SNR) conditions. Firstly, a sparse real-domain representation model is constructed based on the estimated frequency domain response of the OFDM signal physical layer protocol data unit. Then, probability hypothesis for the noise and sparse coefficient vectors are made in the model, and Automatic Relevance Determination (ARD) prior is introduced. Finally, according to the Bayesian framework, the Expectation Maximization (EM) algorithm is used to solve the hyperparameters to estimate the delay. The simulation experiments show that the proposed algorithm has better estimation performance and is closer to the Cramér–Rao Bound (CRB). At the same time, based on the Universal Software Radio Peripheral (USRP), the effectiveness of the proposed algorithm is verified by the actual signal.
Key words:Time delay estimation/
Neural networks/
Automatic Relevance Determination (ARD)/
Universal Software Radio Peripheral (USRP)
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