Frequency offset tracking and estimation algorithm in orthogonal frequency division multiplexing based on improved strong tracking unscented Kalman filter

doi:10.11772/j.issn.1001-9081.2014.08.2248

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (8): 2248-2251.DOI: 10.11772/j.issn.1001-9081.2014.08.2248

• Network and communications • Previous Articles Next Articles

Frequency offset tracking and estimation algorithm in orthogonal frequency division multiplexing based on improved strong tracking unscented Kalman filter

YANG Zhaoyang¹,YANG Xiaopeng¹,LI Teng¹,YAO Kun¹,ZHANG Hengyang¹

Received:2014-03-10 Revised:2014-04-16 Online:2014-08-01 Published:2014-08-10
Contact: YANG Zhaoyang

基于改进强跟踪无迹卡尔曼滤波的正交频分复用频偏跟踪和估计算法

杨朝阳¹,杨霄鹏¹,李腾¹,姚昆¹,²,张衡阳¹

1. 空军工程大学信息与导航学院，西安710077
2.

通讯作者: 杨朝阳
作者简介:杨朝阳(1989-)，男，陕西宝鸡人，硕士研究生，主要研究方向：OFDM频偏估计；杨霄鹏(1973-)，男，甘肃天水人，副教授，博士，主要研究方向：宽带无线通信、信号处理；李腾(1990-)，男，陕西西安人，硕士研究生，主要研究方向：VoIP、时延预测算法；姚昆(1975-)，女，陕西西安人，副教授，硕士，主要研究方向：频谱管理、信号处理；张衡阳(1978-），男，湖南祁东人，副教授，博士，主要研究方向：航空自组网路由算法、数据链抗干扰。
基金资助:
国家自然科学基金资助项目

Abstract

Abstract:

Towards the large frequency offset caused by Doppler effect in high speed moving environment, a dynamic state space model of Orthogonal Frequency Division Multiplexing (OFDM) was built, and a kind of frequency offset tracking and estimation algorithm in OFDM based on improved Strong Tracking Unscented Kalman Filter (STUKF) was proposed. By combining strong tracking filter theory and UKF together, the fading factor was introduced during the process of calculating the measurement predictive covariance and cross covariance. The frequency offset estimation error covariance was adjusted; meanwhile, the process noise covariance was also controlled, and the gain matrix was adjusted in real-time. So the tracking ability to time-varying frequency offset was enhanced and the estimated accuracy was raised. The simulation test was carried out in time-invariant and time-varying frequency offset models. The simulation results show that the proposed algorithm has better tracking and estimation performance than the UKF frequency offset estimation algorithm, the Signal-to-Noise Ratio (SNR) raises about 1dB under the same Bit Error Rate (BER).

摘要：

针对高速运动环境下多普勒效应导致的载波频偏，建立了正交频分复用(OFDM)动态状态空间模型，提出了基于改进的强跟踪无迹卡尔曼滤波(STUKF)的频偏跟踪和估计算法。该算法将强跟踪滤波思想跟UKF相结合，通过在计算量测预测协方差和互协方差时引入渐消因子，在调整前一时刻频偏估计误差协方差的同时又控制过程噪声协方差，实时调整增益矩阵，增强了对时变频偏的跟踪能力，提高了估计精度。最后分别在非时变和时变频偏模型下对所提算法进行了仿真验证。仿真结果表明，与UKF频偏估计算法相比，所提算法在时变频偏中具有更好的跟踪和估计性能，在相同误码率(BER)下信噪比(SNR)大约有1dB的提升。

CLC Number:

TN911.7

YANG Zhaoyang YANG Xiaopeng LI Teng YAO Kun ZHANG Hengyang. Frequency offset tracking and estimation algorithm in orthogonal frequency division multiplexing based on improved strong tracking unscented Kalman filter[J]. Journal of Computer Applications, 2014, 34(8): 2248-2251.

杨朝阳杨霄鹏李腾姚昆张衡阳. 基于改进强跟踪无迹卡尔曼滤波的正交频分复用频偏跟踪和估计算法[J]. 计算机应用, 2014, 34(8): 2248-2251.

References

［1］LI D, ZHANG H. Review of key technology of 3G and TD-LTE mobile communication systems for high-speed railway ［J］. Application Research of Computers, 2013, 30(5): 1297-1301.(李丹,庄宏成.高速铁路3G及TD-LTE移动通信关键问题研究综述［J］.计算机应用研究,2013,30(5):1297-1301.)
［2］CHEN Q, YANG X, OUYANG C, et al. Estimation of carrier frequency offset in aeronautical mobile communication OFDM system based on TD-NLMS algorithm ［J］. Journal of Air Force Engineering University: Natural Science Edition, 2013, 14(1): 71-74.(陈强,杨霄鹏,欧阳超,等.基于TD-NLMS的航空移动通信OFDM系统载波频率偏移消除［J］.空军工程大学学报:自然科学版,2013,14(1):71-74.)
［3］ZHAO Y, HAGGMAN S. Inter-carrier interference self-cancellation scheme for OFDM mobile communication systems ［J］. IEEE Transactions on Communications, 2001, 49(7): 1185-1191.
［4］PENG Y-H, KUO Y-C, LEE G-R, et al. Performance analysis of a new ICI self-cancellation scheme in OFDM systems ［J］. IEEE Transactions on Consumer Electronics, 2007, 53(4): 1333-1338.
［5］WANG C, HUANG Y. Inter-carrier interference cancellation using general phase rotated conjugate transmission for OFDM systems ［J］. IEEE Transactions on Communications, 2010, 58(3): 812-819.
［6］TSENG S-M, HUANG Y-S. A novel ICI self-cancellation scheme for OFDM systems ［J］. International Journal of Communication Systems, 2011, 24(11): 1496-1505.
［7］YU J, SU Y. Pilot-assisted maximum-likelihood frequency-offset estimation for OFDM systems ［J］. IEEE Transactions on Communications, 2004, 52(11): 1997-2008.
［8］ZHAO P, KUANG L, LU J. Carrier frequency offset estimation using extended Kalman filter in uplink OFDMA systems ［C］// Proceedings of the 2006 IEEE International Conference on Communications. Washington, DC: IEEE Computer Society, 2006: 2870-2874.
［9］ZHANG X, YE F, YANG B, et al. Application of Sigma-Point Kalman filter to carrier frequency offset estimation of OFDM system ［J］. Journal of Tianjin University: Science and Technology, 2013, 46(5): 458-462.(张鑫明,叶锋,杨波,等.Sigma-Point卡尔曼滤波用于OFDM载波频偏估计［J］.天津大学学报:自然科学与工程技术版,2013,46(5):458-462.)
［10］ZHANG X M, YANG B, LI S. An unscented Kalman filter for ICI cancellation in high-mobility OFDM system ［C］// Proceedings of the IEEE 73th Vehicular Technology Conference. Washington, DC: IEEE Computer Society, 2011: 1-5.
［11］JULIER S, UHLMANN J, DURRANT-WHYTE H F. A new method for the nonlinear transformation of means and covariance in filters and estimators ［J］. IEEE Transactions on Automatic Control, 2000, 45(3): 477-482.
［12］GUO Z, MIAO L, ZHAO H. An improved strong tracking UKF and the application in initial alignment under large azimuth misalignment angles［J］. Acta Aeronautica et Astronautica Sinica, 2014, 35(1): 203-214.(郭泽,缪玲娟,赵洪松.一种改进的强跟踪UKF算法及其在SINS大方位失准角初始对准中的应用［J］.航空学报,2014,35(1):203-214.)
［13］WANG X, ZHAO L, XIA Q, et al. Strong tracking filter based on unscented transformation ［J］. Control and Decision, 2010, 25(7): 1063-1068.(王小旭,赵琳,夏全喜,等.基于Unscented变换的强跟踪滤波器［J］.控制与决策,2010,25(7):1063-1068.)

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Frequency offset tracking and estimation algorithm in orthogonal frequency division multiplexing based on improved strong tracking unscented Kalman filter

基于改进强跟踪无迹卡尔曼滤波的正交频分复用频偏跟踪和估计算法

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