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中文摘要：
针对现有正交频分复用（OFDM）系统高斯粒子滤波频偏估计算法复杂度高、实时性较差的不足,结合OFDM系统状态空间线性非线性混合的特点,提出了一种新的基于混合高斯粒子滤波的OFDM频偏估计算法。该算法采用状态空间模型对其进行了建模分析,通过将卡尔曼滤波与高斯粒子滤波相结合,在时间更新阶段直接更新高斯分布参数代替逐个粒子更新,在量测更新阶段采用线性变换法生成采样粒子群代替传统高斯采样过程,降低了滤波复杂度,避免了粒子退化和贫化问题。仿真实验和分析表明,该算法在保证滤波精度、快速收敛的同时有效地减少了算法运行时间,提高了系统实时性。
英文摘要：
To solve the problems of high complexity, poor real-time of the Gauss particle filter carrier frequency offset estimation algorithm, for the property of OFDM system state-space model linearity and nonlinearity, a new fast sampling hybrid Gaussian particle filtering algorithm was proposed. Kalman filtering was combined with Gaussian particle filtering. During the time updating process, the parameters of Gaussian distribution was updated by instead of the each particle updating. During the measurement updating process, the linear transfor-mation method was adopted to produce particles instead of traditional Gaussian sampling particle method. The complexity was decreased effectively and the particle impoverishment and dilution effect were avoided. The simulation results demonstrate the proposed algorithm while ensuring the accuracy and convergence speed, and could reduce the complexity and improve the real-time property.
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