增强粒子滤波船舶组合导航算法

针对粒子滤波作为非线性/非高斯估计方法存在的粒子退化和贫化的问题,提出了一种基于集合卡尔曼滤波(Ensemble Kalman filter,EnKF)和马尔可夫蒙特卡罗(Markov Chain Monte Carlo,MCMC)的增强粒子滤波算法。首先,使用EnKF分析代替先验密度对PF的建议密度进行定义,从而降低粒子退化的风险;其次,当发生粒子退化时,通过MCMC方法进行重采样,以增加粒子的多样性,从而降低了粒子贫化的可能性,提高滤波器的精度;最后,将提出的方法应用到GPS PPP/INS组合导航系统中,实验结果均表明,增强粒子滤波算法能提高估计精度,其性能优于标准粒子滤波。

Ship Integrated Navigation Algorithm Based on Enhanced Particle Filter

To solve the problem of particle degeneracy and particle impoverishment in the nonlinear/non-Gaussian estimation of particle filter method,an enhanced particle filter algorithm based on Ensemble Kalman filter(EnKF)and Markov chain Monte Carlo(MCMC)is proposed.Firstly,EnKF analysis is used to define the proposed density of PF instead of prior density to reducing the risk of particle degeneracy;secondly,to reduce the possibility of particle impoverishment and improve the accuracy of filter,resampling is carried out by MCMC method to increase the diversity of particles when particle degeneracy occurs;finally,the proposed method is applied to GPS PPP/INS integrated navigation system,and the experimental results show that the enhanced particle filter algorithm can improve the estimation accuracy,and its performance is better than standard particle filter.