scholarly journals Data Assimilation with An Improved Particle Filter and Its Application in TRIGRS Landslide Model

2018 ◽  
Author(s):  
Changhu Xue ◽  
Guigen Nie ◽  
Haiyang Li ◽  
Jing Wang
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
State Space Models ◽  
Gaussian State ◽  
Mean Square ◽  
Assimilation Experiment ◽  
Particle Filter Algorithm ◽  
Mean Square Difference ◽  
Non Gaussian ◽  
Landslide Model

Abstract. Particle filter has become a popular algorithm in data assimilation for its capability to handle non-linear or non-Gaussian state-space models, while it still be seriously influenced by its disadvantages. In this work, the particle filter algorithm is improved, proposed two methods to overcome the particle degeneration and improve particles’ efficiency. In this algorithm particle-propagating and resample method are ameliorated. The new particle filter is applied to Lorenz-63 model, verified its feasibility and effectiveness using only 20 particles. The root mean square difference(RMSD) of estimations converge to stable when there are more than 20 particles. Finally, we choose a 10 * 10 grid slope model of TRIGRS and carry out an assimilation experiment. Results show that the estimations of states can effectively correct the running-offset of the model and the RMSD is convergent after 3 days assimilation.

scholarly journals Data assimilation with an improved particle filter and its application in the TRIGRS landslide model

2018 ◽  
Vol 18 (10) ◽  
pp. 2801-2807 ◽  
Author(s):  
Changhu Xue ◽  
Guigen Nie ◽  
Haiyang Li ◽  
Jing Wang
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Gaussian State ◽  
Assimilation Experiment ◽  
Resampling Method ◽  
Particle Propagation ◽  
Particle Filter Algorithm ◽  
Mean Square Difference ◽  
Non Gaussian ◽  
Landslide Model

Abstract. Particle filters have become a popular algorithm in data assimilation for their ability to handle nonlinear or non-Gaussian state-space models, but they have significant disadvantages. In this work, an improved particle filter algorithm is proposed. To overcome the particle degeneration and improve particles' efficiency, the processes of particle resampling and particle transfer are updated. In this improved algorithm, particle propagation and the resampling method are ameliorated. The new particle filter is applied to the Lorenz-63 model, and its feasibility and effectiveness are verified using only 20 particles. The root-mean-square difference (RMSD) of estimations converges to stable when there are more than 20 particles. Finally, we choose a peristaltic landslide model and carry out an assimilation experiment of 20 days. Results show that the estimations of states can effectively correct the running offset of the model and the RMSD is convergent after 3 days of assimilation.

Bayesian Inference for Time Series State Space Models

2011 ◽  
pp. 59-124 ◽  
Author(s):  
Paolo Giordani ◽  
Michael Pitt ◽  
Robert Kohn
Keyword(s):  
Time Series ◽  
Particle Filter ◽  
State Space ◽  
Empirical Work ◽  
State Space Models ◽  
Gaussian State ◽  
Advantages And Disadvantages ◽  
Nonlinear State Space Models ◽  
Non Gaussian ◽  
Main Ideas

This article provides a description of time series methods that emphasize modern macroeconomics and finance. It discusses a variety of posterior simulation algorithms and illustrates their use in a range of models. This article introduces the state space framework and explains the main ideas behind filtering, smoothing, and likelihood computation. It also mentions the particle filter as a general approach for estimating state space models and gives a brief discussion of its methods. The particle filter is a very useful tool in the Bayesian analysis of the kinds of complicated nonlinear state space models that are increasingly being used in macroeconomics. It also deals with conditionally Gaussian state space models and non-Gaussian state space models. A discussion of the advantages and disadvantages of each algorithm is provided in this article. This aims to help with the use of these methods in empirical work.

scholarly journals DETECTION LIMITS FOR LINEAR NON-GAUSSIAN STATE-SPACE MODELS

2006 ◽  
Vol 39 (13) ◽  
pp. 282-287 ◽  
Author(s):  
Gustaf Hendeby ◽  
Fredrik Gustafsson
Keyword(s):  
State Space ◽  
Detection Limits ◽  
State Space Models ◽  
Gaussian State ◽  
Non Gaussian

scholarly journals Predicting crypto-currencies using sparse non-Gaussian state space models

2018 ◽  
Vol 37 (6) ◽  
pp. 627-640 ◽  
Author(s):  
Christian Hotz-Behofsits ◽  
Florian Huber ◽  
Thomas Otto Zörner
Keyword(s):  
State Space ◽  
State Space Models ◽  
Gaussian State ◽  
Non Gaussian

scholarly journals IMM Iterated Extended Particle Filter Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yang Wan ◽  
Shouyong Wang ◽  
Xing Qin
Keyword(s):  
Particle Filter ◽  
Density Function ◽  
The State ◽  
Arbitrary Distribution ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Maneuvering Target ◽  
Particle Filter Algorithm ◽  
Non Gaussian ◽  
Importance Density Function

In order to solve the tracking problem of radar maneuvering target in nonlinear system model and non-Gaussian noise background, this paper puts forward one interacting multiple model (IMM) iterated extended particle filter algorithm (IMM-IEHPF). The algorithm makes use of multiple modes to model the target motion form to track any maneuvering target and each mode uses iterated extended particle filter (IEHPF) to deal with the state estimation problem of nonlinear non-Gaussian system. IEHPF is an improved particle filter algorithm, which utilizes iterated extended filter (IEHF) to obtain the mean value and covariance of each particle and describes importance density function as a combination of Gaussian distribution. Then according to the function, draw particles to approximate the state posteriori density of each mode. Due to the high filter accuracy of IEHF and the adaptation of system noise with arbitrary distribution as well as strong robustness, the importance density function generated by this method is more approximate to the true sate posteriori density. Finally, a numerical example is included to illustrate the effectiveness of the proposed methods.

scholarly journals Review article: Comparison of local particle filters and new implementations

2018 ◽  
Vol 25 (4) ◽  
pp. 765-807 ◽  
Author(s):  
Alban Farchi ◽  
Marc Bocquet
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Particle Filtering ◽  
Transport Theory ◽  
Optimal Transport ◽  
Curse Of Dimensionality ◽  
Local Analysis ◽  
Problem Size ◽  
Yield Data ◽  
Non Gaussian

Abstract. Particle filtering is a generic weighted ensemble data assimilation method based on sequential importance sampling, suited for nonlinear and non-Gaussian filtering problems. Unless the number of ensemble members scales exponentially with the problem size, particle filter (PF) algorithms experience weight degeneracy. This phenomenon is a manifestation of the curse of dimensionality that prevents the use of PF methods for high-dimensional data assimilation. The use of local analyses to counteract the curse of dimensionality was suggested early in the development of PF algorithms. However, implementing localisation in the PF is a challenge, because there is no simple and yet consistent way of gluing together locally updated particles across domains. In this article, we review the ideas related to localisation and the PF in the geosciences. We introduce a generic and theoretical classification of local particle filter (LPF) algorithms, with an emphasis on the advantages and drawbacks of each category. Alongside the classification, we suggest practical solutions to the difficulties of local particle filtering, which lead to new implementations and improvements in the design of LPF algorithms. The LPF algorithms are systematically tested and compared using twin experiments with the one-dimensional Lorenz 40-variables model and with a two-dimensional barotropic vorticity model. The results illustrate the advantages of using the optimal transport theory to design the local analysis. With reasonable ensemble sizes, the best LPF algorithms yield data assimilation scores comparable to those of typical ensemble Kalman filter algorithms, even for a mildly nonlinear system.

scholarly journals Pipeline Gaussian Particle Filter and Hardware Design for Attitude Estimation with UAV

2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Yali Xue ◽  
Hu Chen ◽  
Jie Chen ◽  
Jiahui Wang
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Gaussian Noise ◽  
Estimation Algorithm ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Gaussian State ◽  
Time Performance ◽  
Simulation Results ◽  
Non Gaussian

This paper based on the Gaussian particle filter (GPF) deals with the attitude estimation of UAV. GPF algorithm has better estimation accuracy than the general nonlinear non-Gaussian state estimation and is usually used to improve the system’s real-time performance whose noise is specific such as Gaussian noise during the mini UAV positioning and navigation. The attitude estimation algorithm is implemented on FPGA to verify the effectiveness of the Gaussian particle filter. Simulation results have illustrated that the GPF algorithm is effective and has better real-time performance than that of the particle filter.

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