基于高斯厄米特粒子滤波算法的多用户检测

Multi-User Detection Based on Gauss-Hermite Particle Filter

标准粒子滤波存在粒子退化的缺陷,高斯厄米特粒子滤波算法可以缓解这个缺陷。新算法中,利用高斯厄米特滤波来产生重要性概率密度函数,该概率密度函数在系统状态的转移概率基础上加入新的观测数据,更接近于系统状态的后验概率。理论分析和实验结果表明:在相同的情况下,高斯厄米特粒子滤波算法优于标准粒子滤波算法。

A major problem of particle filter is particle degradation. Gauss-Hermite particle filter （GHPF） algorithm function was proposed in this paper for relieving the defects. In this new algorithm, a bank of Gauss-Hermite filter is used for generating the importance density function. The density function integrates the new observations into system state transition density, so it can match the state posteriori density well. The theoretical analysis and experimental results show that the new particle filter outperforms obviously the standard particle filter in same conditions.