文献类型：期刊文献

中文题名：基于卡尔曼滤波的快时变稀疏信道估计新技术

英文题名：Fast Time-Varying Sparse Channel Estimation Based on Kalman Filter

作者：袁伟娜[1];王嘉璇[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2018

卷号：53

期号：4

起止页码：835

中文期刊名：西南交通大学学报

外文期刊名：Journal of Southwest Jiaotong University

收录：CSTPCD;;EI(收录号：20184305974957);Scopus;北大核心:【北大核心2017】；CSCD:【CSCD2017_2018】；

基金：国家自然科学基金资助项目(61501187);中央高校基本科研业务费专项基金资助项目(222201714032)

语种：中文

中文关键词：信道估计;基扩展模型;卡尔曼滤波;压缩感知

外文关键词：channel estimation;basic expansion model;Kalman filter;compressed sensing

摘要：针对高铁以及山区环境下正交频分复用(orthogonal frequency division multiplexing,OFDM)通信系统的信道估计问题,提出一种基于卡尔曼滤波的快时变稀疏信道估计方法.该方法基于快时变信道的基扩展模型(basic expansion model,BEM),应用压缩感知(compressed sensing,CS)理论进行稀疏时延估计,并应用卡尔曼滤波(Kalman filter,KF)技术对BEM系数进行估计,进而获得信道增益.仿真结果表明,在相同信噪比(signal to noise ratio,SNR)条件下,随着归一化多普勒频移(frequency-normalized Doppler shift,FND)增大,新方法的信道估计均方差(mean square error,MSE)性能优于传统方法,如当SNR为20 dB,FND为0.1时,新方法较传统方法性能提升了4 dB,表明对信道时变性具有更优的鲁棒性;在相同的多普勒频移条件下,随着SNR增加,各方法的均方差均有所改善,新方法改善更明显,如当FND为0.2时,在信道估计均方差为0.06的条件下,新方法较传统方法获得了6 dB的信噪比增益,表明对抗信道噪声能力更强.
A fast time-varying sparse channel estimation method based on the Kalman filter is proposed for channel estimation of an orthogonal frequency division multiplexing communication system operating in high-speed railways and mountain areas.Based on the basic expansion model(BEM),compressed sensing(CS)was employed for the estimation of sparse delays,and a Kalman filter(KF)estimator was utilised for estimating the BEM coefficients.The channel gains were then computed easily.The simulation results show that under the same signal-to-ratio(SNR)condition,with the increase in frequency-normalised Doppler shift(FND),the MSE of the new method is superior to that of traditional methods,such as SNR is 20 dB and FND is 0.1,and a 4 dB performance improvement is achieved.Under the same Doppler shift condition,the same result is obtained as that with the increase in SNR,such as FND is 0.2 and MSE is 0.06,and a 6 dB SNR gain is achieved.These results show that the new method is more robust to variation in channel time and stronger against noise compared with traditional methods.