【摘要】 针对观测存在野值的非线性系统滤波问题,本文综合运用多高斯和近似算法(GSA)、贝叶斯公式、马尔科夫链蒙特卡洛算法(MCMC)以及集合卡尔曼滤波算法(EnKF)设计了一种能从状态后验分布中抽取粒子的改进粒子滤波算法,并根据模糊理论为此改进算法设计了模糊抗野值功能,从而提出了模糊抗野值集合粒子滤波算法,命名为REnPF。GPS辅助SINS行进间对准的仿真实验表明,REnPF能够很好地避免虚警、漏检问题,并能提供良好的滤波精度。更多还原

【Abstract】 When a global positioning system(GPS) aided strapdown inertial navigation system(SINS) aligns in-motion, the filtering system will be nonlinear because of the large initial attitude error. In addition, when the GPS signals are disturbed, the outliers appearing in the observation will reduce the filtering accuracy and even cause the filtering divergence. Aiming at the problem of filtering for nonlinear systems with outliers, an improved particle filter algorithm is designed in this paper, which can extract particles from the posterior distribution of states by using Gaussian sum approximation algorithm(GSA), Bayesian formula, Markov chain Monte Carlo algorithm(MCMC) and ensemble Kalman filter algorithm(EnKF) synthetically. Furthermore, according to the fuzzy theory, an outlier constraint function is added into the improved algorithm to construct the robust ensemble particle filter(REnPF) proposed in this paper. The simulation results of the GPS aided SINS in-motion alignment show that the REnPF can effectively avoid false alarm and missing detection problems, and provide good filtering accuracy.更多还原