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.

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 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.

A New Adaptive UPF Algorithm through Improved Relative Entropy

2013 ◽  
Vol 658 ◽  
pp. 569-573
Author(s):  
Wen Tao Yu ◽  
Jun Peng ◽  
Xiao Yong Zhang
Keyword(s):  
Particle Filter ◽  
Relative Entropy ◽  
Particle Number ◽  
Time Step ◽  
Unscented Particle Filter ◽  
Posterior Probability Density ◽  
The Difference ◽  
Particle Filter Algorithm ◽  
Non Gaussian ◽  
Number Of Particles

Unscented particle filter (UPF) has high accuracy of state estimation for nonlinear system with non-Gaussian noise. While the computation of traditional unscented particle filter is huge and this depends on the particle number. In this paper we propose a new adaptive unscented particle filter algorithm AUPF through improved relative entropy which can adaptively adjust the particle number during filtering. Firstly the relative entropy is used to measure the distance between the posterior probability density and the importance proposal and the least number of particles for the next time step is decided according to the relative entropy. Then the least number is adjusted to offset the difference between the importance proposal and the true distribution. This algorithm can effectively reduce unnecessary particles meanwhile reduce the computation. The simulation results show the effectiveness of AUPF.

scholarly journals Simplified partial resampling method for state estimation usingparticle filter

2018 ◽  
Vol 7 (2.7) ◽  
pp. 243
Author(s):  
M Tirumala Reddy ◽  
Y Sri Ganesh ◽  
Ch Lakshmi Gayathri ◽  
T Megha Shyam ◽  
S Koteswar Rao ◽  
...  
Keyword(s):  
Particle Filter ◽  
State Estimation ◽  
Gaussian Noise ◽  
The State ◽  
Resampling Methods ◽  
Simple Pendulum ◽  
Filter Methods ◽  
Resampling Method ◽  
Paper Work ◽  
Non Gaussian

Particle filter methods are used in the estimation and tracking of the objects for non-linear and non-gaussian noise conditions. In this paper work the object estimation using partial resampling methods are discussed. On using partial resampling method resampling becomes faster. The performance of particle filter with partial resampling scheme is analyzed using the state estima-tion of a simple pendulum.

The Signal Separation for MIMO Radar Based on Particle Filter Algorithm

2014 ◽  
Vol 536-537 ◽  
pp. 34-38
Author(s):  
Yan Ping Zhu ◽  
Ke Tang ◽  
Jia Qiang Li ◽  
Jin Li Chen
Keyword(s):  
Particle Filter ◽  
Mimo Radar ◽  
Radar System ◽  
Signal Separation ◽  
Separation Performance ◽  
Good Separation ◽  
Match Filter ◽  
Chaotic Signals ◽  
Particle Filter Algorithm ◽  
Non Gaussian

The orthogonality of transmitting signals affects the performance of Muitple Input Mulitiple Output (MIMO) radar system. The chaotic signals was adopted at the transmitter to achieve the approximate orthogonal. A signal separation aprroach for MIMO Radar based on the particle filter in Non-Gaussian clutter environment was proposed. Before the match filter (MF), the particle filter is quite suitable for chaotic signals separation. Simulation results show that the proposed algorithm can realize a good separation performance. At the receiver, the coherent processing results show that this method has the better target resolution ability than the tranditional match filter alone.

A Novel Modified Particle Filter Algorithm

2011 ◽  
Vol 204-210 ◽  
pp. 1895-1899
Author(s):  
Qing Hua Gao ◽  
Jie Wang ◽  
Ming Lu Jin
Keyword(s):  
Probability Distribution ◽  
Particle Filter ◽  
State Estimation ◽  
Target Tracking ◽  
Optimization Method ◽  
Effective Solution ◽  
Optimal Probability ◽  
Particle Filter Algorithm ◽  
Non Gaussian ◽  
Filtering Problem

The particle filter (PF) algorithm provides an effective solution to the non-linear and non-Gaussian filtering problem. However, when the motion noises or observation noises are strong, the degenerate phenomena will occur, which leads to poor estimation. In this paper, we propose a modified particle filter (MPF) algorithm for improving the estimated precision through a particle optimization method. After calculating the coarse estimation with the traditional PF, we optimize the particles according to their weights and relative positions, then, move the particles toward the optimal probability distribution. The state estimation and target tracking experiments demonstrate the outstanding performance of the proposed algorithm.

The Particle Filter Algorithm Research for Target Tracking Based on Information Fusion

2012 ◽  
Vol 628 ◽  
pp. 440-444 ◽  
Author(s):  
Juan Li ◽  
Hui Juan Hao ◽  
Mao Li Wang
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Information Fusion ◽  
Particle Filtering ◽  
Tracking System ◽  
Distributed Sensor ◽  
Multi Target Tracking ◽  
Particle Filter Algorithm ◽  
Gaussian System ◽  
Non Gaussian

This paper researches the particle filters Algorithms for target tracking based on Information Fusion, it combines the traditional Kalman filter with the particle filter. For multi-sensor and multi-target tracking system with complex application background, which is nonlinear and non-gaussian system, the paper proposes an effective particle filtering algorithm based on information fusion for distributed sensor, this algorithm contributes to the solution of particle degradation problems and the phenomenon of particle lack, and achieve high precision for target tracking.

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