机译:基于集合卡尔曼滤波和马尔可夫链蒙特卡罗方法的改进粒子滤波算法
Inst. of Remote Sensing & Digital Earth, Beijing, China;
Gaussian distribution; Kalman filters; Markov processes; Monte Carlo methods; data assimilation; geophysics computing; moisture; particle filtering (numerical methods); soil; Earth science research; Gaussian error distributions; Markov Chain Monte Carlo method; NaQu network region; Tibetan Plateau; advanced microwave scanning radiometer; brightness temperature assimilation; data assimilation methods; ensemble Kalman filter analysis; linear models; particle degeneracy risk; particle diversity; particle filter algorithm; soil moisture estimatiion; variance infiltration capacity model; Data models; Kalman filters; Proposals; Soil moisture; Standards; Vegetation; Data assimilation (DA); Markov Chain Monte Carlo (MCMC); Markov Chain Monte Carlo (MCMC); ensemble Kalman filter (EnKF); particle filter (PF);
机译:将集成卡尔曼滤波器与马尔可夫链蒙特卡洛相结合以改进历史匹配和不确定性表征
机译:基于次梯度的马尔可夫链蒙特卡罗粒子法用于离散时间非线性滤波
机译:使用粒子滤波-马尔可夫链蒙特卡罗方法进行整体数据同化的不确定性量化
机译:基于马尔可夫链蒙特卡罗的改进扩展卡尔曼粒子滤波器的非线性状态估计。
机译:集合卡尔曼滤波和两阶段马尔可夫链蒙特卡罗方法对储层模型进行多级连续数据同化和不确定性量化
机译:基于Cubature卡尔曼滤波的机动目标跟踪的交互式多模型滤波改进算法。
机译:基于次梯度的马尔可夫链蒙特卡罗粒子法用于离散时间非线性滤波