State estimation of conceptual hydrological models using unscented Kalman filter

2018 ◽  
Vol 50 (2) ◽  
pp. 479-497 ◽  
Author(s):  
P. Jiang ◽  
Y. Sun ◽  
W. Bao
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Hydrologic Model ◽  
Generation Process ◽  
Hydrological Models ◽  
State Variables ◽  
Symmetric Point ◽  
The Difference

Abstract Unscented Kalman filter (UKF) has its origin in transforming the Gaussian random variables for nonlinear estimation and has received little attention in the context of state estimation of conceptual hydrological models. This paper introduces UKF to estimate state variables of a conceptual hydrologic model. A symmetric point approach and a scaling framework are used for performing the sample generation process of UKF. This paper investigates the application of UKF for state estimation with a synthetic case study in which both the simulated state, the true state, and the corrected state are precisely known. The results show that the use of UKF can improve the performance of both the model outputs and the state variables as the difference between the corrected trajectories and the true trajectories decreases rapidly and tends to vanish after only a few iterations. Our results and comparisons also demonstrated the capability and usefulness of UKF for state estimation in two real basins.

Power System State Estimation by Novel Approach of Kalman Filter

2017 ◽  
Vol 6 (2) ◽  
pp. 241
Author(s):  
Arpit Khandelwal ◽  
Ankush Tandon ◽  
Akash Saxena
Keyword(s):  
Kalman Filter ◽  
Power System ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Test System ◽  
Electrical Network ◽  
Elapse Time ◽  
State Variables ◽  
Measuring Device ◽  
M Estimator

<p>The electrical network measurements by measuring device Phasor Measurement Device (PMU) are usually sent to the control centers using data acquisition system and other communication protocols available. However, these measurements contain uncertainties due to the measurements and communication noise (errors), incomplete metering or unavailability of some of measurements. The overall aim of state estimation is to calculate the state variables of the power system by minimizing errors available at the control center. Due to generate desired quantities by optimal estimate which is given the set of measurements, Kalman filters are widely used. This paper discusses the application of an Extended Kalman Filter (EKF) algorithm, the Unscented Kalman Filter (UKF) algorithm, and New EKF+M and UKF+M estimator algorithm, those are modification of EKF and UKF for enhance accuracy and elapse time is less. The effectiveness and performance of EKF+M and UKF+M Estimator over another Filtration algorithm is shown. These state estimation techniques applied on IEEE-30 bus, 14 bus and 9 bus test system.</p>

scholarly journals Parameters Estimation and Prediction of Water Movement and Solute Transport in Layered, Variably Saturated Soils Using the Ensemble Kalman Filter

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1520
Author(s):  
Zheng Jiang ◽  
Quanzhong Huang ◽  
Gendong Li ◽  
Guangyong Li
Keyword(s):  
Kalman Filter ◽  
Solute Transport ◽  
Ensemble Kalman Filter ◽  
Water Movement ◽  
Parameters Estimation ◽  
Saturated Soils ◽  
Hydrological Models ◽  
State Variables ◽  
Ensemble Size ◽  
Highly Nonlinear

The parameters of water movement and solute transport models are essential for the accurate simulation of soil moisture and salinity, particularly for layered soils in field conditions. Parameter estimation can be achieved using the inverse modeling method. However, this type of method cannot fully consider the uncertainties of measurements, boundary conditions, and parameters, resulting in inaccurate estimations of parameters and predictions of state variables. The ensemble Kalman filter (EnKF) is well-suited to data assimilation and parameter prediction in Situations with large numbers of variables and uncertainties. Thus, in this study, the EnKF was used to estimate the parameters of water movement and solute transport in layered, variably saturated soils. Our results indicate that when used in conjunction with the HYDRUS-1D software (University of California Riverside, California, CA, USA) the EnKF effectively estimates parameters and predicts state variables for layered, variably saturated soils. The assimilation of factors such as the initial perturbation and ensemble size significantly affected in the simulated results. A proposed ensemble size range of 50–100 was used when applying the EnKF to the highly nonlinear hydrological models of the present study. Although the simulation results for moisture did not exhibit substantial improvement with the assimilation, the simulation of the salinity was significantly improved through the assimilation of the salinity and relative solutetransport parameters. Reducing the uncertainties in measured data can improve the goodness-of-fit in the application of the EnKF method. Sparse field condition observation data also benefited from the accurate measurement of state variables in the case of EnKF assimilation. However, the application of the EnKF algorithm for layered, variably saturated soils with hydrological models requires further study, because it is a challenging and highly nonlinear problem.

scholarly journals Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation

Sensors ◽  
2016 ◽  
Vol 16 (9) ◽  
pp. 1530 ◽  
Author(s):  
Xi Liu ◽  
Hua Qu ◽  
Jihong Zhao ◽  
Pengcheng Yue ◽  
Meng Wang
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Relative State

scholarly journals Iterative Truncated Unscented Particle Filter

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 214
Author(s):  
Yanbo Wang ◽  
Fasheng Wang ◽  
Jianjun He ◽  
Fuming Sun
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Process Models ◽  
Filter Method ◽  
Proposal Distribution ◽  
Truncation Method ◽  
Unscented Particle Filter

The particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems. Incorporating constraints could improve the state estimation performance compared to unconstrained state estimation. This paper introduces an iterative truncated unscented particle filter, which provides a state estimation method with inequality constraints. In this method, the proposal distribution is generated by an iterative unscented Kalman filter that is supplemented with a designed truncation method to satisfy the constraints. The detailed iterative unscented Kalman filter and truncation method is provided and incorporated into the particle filter framework. Experimental results show that the proposed algorithm is superior to other similar algorithms.

Sign in / Sign up

Export Citation Format

Share Document