【摘要】 针对非线性、非高斯系统状态的在线估计问题,提出一种新的基于序贯重要性抽样的粒子滤波算法.在滤波算法中,用状态参数分解和退火系数来产生重要性概率密度函数,此概率密度函数综合考虑了转移先验、似然、噪声的统计特性以及最新的观察数据,因此更接近于系统状态的后验概率.理论分析与仿真实验表明该粒子滤波器的性能明显优于标准的粒子滤波器和扩展卡尔曼滤波器.更多还原

【Abstract】 In this paper, a new particle filter based on sequential importance sampling (SIS) is proposed for the on_line estimation of non_Gaussian nonlinear systems. In this filtering method, state parameters separation and an annealing parameter are used to produce importance function. Since the distribution function makes full use of the prior, likelihood, and statistical characteristics of noise and the newest observation data, it is much closer to posterior distributions. Theoretical analysis and simulation show that the performance of proposed particle filter outperforms the standard particle filter and the extended Kalman filter.更多还原