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中文摘要：
针对非高斯、非线性的正交频分复用（OFDM）系统下的符号同步定时校正，常规的卡尔曼滤波算法不能直接解决非线性、非高斯的问题，因而提出一种序列蒙特卡罗（SMC）算法．首先将符号定时偏差以及系统的观测方程建模为动态的系统传递模型；然后用加权的离散随机样本点近似所关注的后验概率密度函数，在频率选择性多径信道估计和跟踪OFDM系统符号定时变化．从仿真结果可以看出，SMC算法在相同信噪比（SNR）下的误比特率（BER）及定时均方误差（MSE）均好于扩展卡尔曼滤波（EKF）算法．
英文摘要：
For the case of symbol timing in nonlinear and non-Gaussian orthogonal frequency division multiplexing （OFDM） systems, it is usually difficult to directly utilize Kalman filter. Thereafter, a feasible structure known as sequential Monte Carlo （SMC） is brought forward. Estimation and tracking the symbol timing offset in frequency selective multi-paths fading channels based on the application of SMC technique is investigated. The problems of symbol timing offset and system observation function are represented as dynamic state-space models and recursive computation of relevant probability distributions using the concepts of importance sampling and approximation of probability distributions with discrete random measures. The performance of the SMC technique and extend Kalman filter （EKF） are compared in terms of bit error rate （BER） and mean square error （MSE） obtained by simulations and SMC superior outperforms EKF for the considered signal noise ratio （SNR）.
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