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针对弱观测噪声条件下非线性、非高斯动态系统的滤波问题,提出一种基于支持向量机的似然粒子滤波算法.首先,采用似然函数作为提议分布,融入最新的观测信息,比采用先验转移密度的一般粒子滤波算法更接近状态的真实后验密度;然后,利用当前粒子及其权值,使用支持向量机估计出状态的后验概率密度模型;最后,根据此模型重采样更新粒子集,有效地克服粒子退化现象并提高状态估计精度.仿真结果表明了所提出算法的可行性和有效性.
For the filtering problem of nonlinear and non-Gaussian dynamic systems under weak observation noise conditions, a new kind of likelihood particle filtering algorithm based on support vector machine is proposed.Firstly, using the likelihood function as the proposed distribution and incorporating the latest observation information, Then, using the current particle and its weights, the support vector machine is used to estimate the posterior probability density model of the state. Finally, the particle is resampled and updated according to the model Which can effectively overcome the particle degradation and improve the state estimation accuracy.The simulation results show the feasibility and effectiveness of the proposed algorithm.