scholarly journals Evolution of ensemble data assimilation for uncertainty quantification using the particle filter-Markov chain Monte Carlo method

2012 ◽  
Vol 48 (12) ◽  
Author(s):  
Hamid Moradkhani ◽  
Caleb M. DeChant ◽  
Soroosh Sorooshian
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Method ◽  
Data Assimilation ◽  
Particle Filter ◽  
Uncertainty Quantification ◽  
Ensemble Data Assimilation ◽  
Ensemble Data

scholarly journals Genetic Operator-Based Particle Filter Combined with Markov Chain Monte Carlo for Data Assimilation in a Crop Growth Model

Agriculture ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 606
Author(s):  
Alaa Jamal ◽  
Raphael Linker
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Data Assimilation ◽  
Particle Filter ◽  
Sequential Estimation ◽  
Measurement Noise ◽  
Genetic Operators ◽  
True Value

Particle filter has received increasing attention in data assimilation for estimating model states and parameters in cases of non-linear and non-Gaussian dynamic processes. Various modifications of the original particle filter have been suggested in the literature, including integrating particle filter with Markov Chain Monte Carlo (PF-MCMC) and, later, using genetic algorithm evolutionary operators as part of the state updating process. In this work, a modified genetic-based PF-MCMC approach for estimating the states and parameters simultaneously and without assuming Gaussian distribution for priors is presented. The method was tested on two simulation examples on the basis of the crop model AquaCrop-OS. In the first example, the method was compared to a PF-MCMC method in which states and parameters are updated sequentially and genetic operators are used only for state adjustments. The influence of ensemble size, measurement noise, and mutation and crossover parameters were also investigated. Accurate and stable estimations of the model states were obtained in all cases. Parameter estimation was more challenging than state estimation and not all parameters converged to their true value, especially when the parameter value had little influence on the measured variables. Overall, the proposed method showed more accurate and consistent parameter estimation than the PF-MCMC with sequential estimation, which showed highly conservative behavior. The superiority of the proposed method was more pronounced when the ensemble included a large number of particles and the measurement noise was low.

Laser Localization of Autonomous Vehicle Using an Improved Particle Filter

2012 ◽  
Vol 629 ◽  
pp. 873-877
Author(s):  
Wen Jian Ying ◽  
Fu Chun Sun
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Method ◽  
Particle Filter ◽  
Probability Density ◽  
Autonomous Vehicle ◽  
Posteriori Probability ◽  
Localization Precision ◽  
Simulation Results

This article presents an improved Rao-Blackwellized particle filter to overcome particles degeneracy phenomenon and acquire the better localization precision of the autonomous vehicle. The joint posteriori probability density is given that being correlative with the position and pose of the autonomous vehicle and the mark characters of the map. The algorithm utilizes a Markov chain Monte Carlo method with the sampling particle of the target to the resample mechanism of the Rao-Blackwellized particle filter. Simulation results show that the improved algorithm is valid.

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