scholarly journals Constrained Dynamic Systems Estimation Based on Adaptive Particle Filter

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Weili Xiong ◽  
Mingchen Xue ◽  
Baoguo Xu
Keyword(s):  
State Estimation ◽  
Nonlinear Model ◽  
Bayesian Approach ◽  
Dynamic Systems ◽  
Constrained Systems ◽  
Computational Time ◽  
Constrained Problems ◽  
Simulation Results ◽  
Non Gaussian ◽  
Bayesian Estimators

For the state estimation problem, Bayesian approach provides the most general formulation. However, most existing Bayesian estimators for dynamic systems do not take constraints into account, or rely on specific approximations. Such approximations and ignorance of constraints may reduce the accuracy of estimation. In this paper, a new methodology for the states estimation of constrained systems with nonlinear model and non-Gaussian uncertainty which are commonly encountered in practice is proposed in the framework of particles filter. The main feature of this method is that constrained problems are handled well by a sample size test and two particles handling strategies. Simulation results show that the proposed method can outperform particles filter and other two existing algorithms in terms of accuracy and computational time.

scholarly journals Bayesian Approach for Distribution System State Estimation With Non-Gaussian Uncertainty Models

2017 ◽  
Vol 66 (11) ◽  
pp. 2957-2966 ◽  
Author(s):  
Paolo Attilio Pegoraro ◽  
Andrea Angioni ◽  
Marco Pau ◽  
Antonello Monti ◽  
Carlo Muscas ◽  
...  
Keyword(s):  
State Estimation ◽  
Bayesian Approach ◽  
Distribution System ◽  
System State ◽  
Non Gaussian ◽  
Uncertainty Models

Multidimensional State Estimation With Multiple Composite Hypothesis Testing in the Presence of Interference

1988 ◽  
Vol 110 (3) ◽  
pp. 297-302
Author(s):  
K. Demirbas
Keyword(s):  
Hypothesis Testing ◽  
State Estimation ◽  
Dynamic Systems ◽  
The State ◽  
Composite Hypothesis ◽  
Time Simulation ◽  
Estimation Scheme ◽  
Component Estimation ◽  
Simulation Results ◽  
Memory Reduction

A new state estimation scheme is presented for multidimensional dynamic systems with arbitrary independent interference and noises, and is based upon quantization, multiple composite hypothesis testing, and a suboptimum decoding technique of Information Theory. The estimation of the state vector is sequentially done, component-by-component, in parallel and in blocks. “Component-by-component” estimation results in a considerable memory reduction for the implementation of the scheme, while estimation is blocks makes the implementation independent of time. Simulation results, some of which are presented, have shown that the new scheme performs well, whereas the classical estimation techniques are not, in general, applicable to the state estimation problem in the presence of arbitrary interference.

A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Scaling Factor ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Novel Method ◽  
Simulation Results ◽  
Tracking Filter ◽  
Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

scholarly journals A Nonlinear Maximum Correntropy Information Filter for High-Dimensional Neural Decoding

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 743
Author(s):  
Xi Liu ◽  
Shuhang Chen ◽  
Xiang Shen ◽  
Xiang Zhang ◽  
Yiwen Wang
Keyword(s):  
State Estimation ◽  
Measurement Model ◽  
High Dimensional ◽  
Neural Firing ◽  
The Neural Network ◽  
Information Filter ◽  
Critical Technology ◽  
Dimensional Measurements ◽  
Non Gaussian ◽  
Low Dimensional

Neural signal decoding is a critical technology in brain machine interface (BMI) to interpret movement intention from multi-neural activity collected from paralyzed patients. As a commonly-used decoding algorithm, the Kalman filter is often applied to derive the movement states from high-dimensional neural firing observation. However, its performance is limited and less effective for noisy nonlinear neural systems with high-dimensional measurements. In this paper, we propose a nonlinear maximum correntropy information filter, aiming at better state estimation in the filtering process for a noisy high-dimensional measurement system. We reconstruct the measurement model between the high-dimensional measurements and low-dimensional states using the neural network, and derive the state estimation using the correntropy criterion to cope with the non-Gaussian noise and eliminate large initial uncertainty. Moreover, analyses of convergence and robustness are given. The effectiveness of the proposed algorithm is evaluated by applying it on multiple segments of neural spiking data from two rats to interpret the movement states when the subjects perform a two-lever discrimination task. Our results demonstrate better and more robust state estimation performance when compared with other filters.

Numerical Simulation of Powder Dispersion Performance by Different Mesh Types

2016 ◽  
Vol 680 ◽  
pp. 82-85
Author(s):  
Jian Cai ◽  
Lan Chen ◽  
Umezuruike Linus Opara
Keyword(s):  
Numerical Simulation ◽  
Flow Field ◽  
Fine Particle ◽  
Particle Deposition ◽  
Tetrahedral Mesh ◽  
Computational Time ◽  
Kinetic Characteristics ◽  
Powder Dispersion ◽  
Simulation Results ◽  
Time Accuracy

OBJECTIVE To investigate the influence of mesh type on numerical simulating the dispersion performance of micro-powders through a home-made tube. METHODS With the computational fluid dynamics (CFD) method, a powder dispersion tube was meshed in three different types, namely, tetrahedral, unstructured hexahedral and prismatic-tetrahedral hybrid meshes. The inner flow field and the kinetic characteristics of the particles were investigated. Results of the numerical simulation were compared with literature evidences. RESULTS The results showed that using tetrahedral mesh had the highest computational efficiency, while employing the unstructured hexahedral mesh obtained more accurate outlet velocity. The simulation results of the inner flow field and the kinetic characteristics of the particles were slightly different among the three mesh types. The calculated particle velocity using the tetrahedral mesh had the best correlation with the changing trend of the fine particle mass in the first 4 stages of the new generation impactor (NGI) (R2 = 0.91 and 0.89 for powder A and B, respectively). Conclusions Mesh type affected computational time, accuracy of simulation results and the prediction abilities of fine particle deposition.

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