Data Assimilation of River Networks Using Extended Kalman Filter

2014 ◽  
Vol 580-583 ◽  
pp. 1923-1927
Author(s):  
Yi Fan Chen ◽  
Jing Lin Qian
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Real Time ◽  
Extended Kalman Filter ◽  
River Network ◽  
Measurement Noise ◽  
River Networks ◽  
Network System ◽  
Process Noise ◽  
Measurement Noises

In order to improve the accuracy of river network hydraulic model, extended kalman filter was used for real-time updating model states. In a simulation example of a river network composed of 14 channels, it systematically analyzed the effects of process and measurement noises on state correction. The results show that the extended kalman filter is able to effectively carry out data assimilation of non-linear river network system, and big process noise in combination with relatively small measurement noise is recommended for state correction.

scholarly journals An Improved Adaptive Extended Kalman Filter Algorithm of SINS/GPS Loosely-Coupled Integrated Navigation System

2018 ◽  
Vol 7 (4.27) ◽  
pp. 87
Author(s):  
Yuyan Wang ◽  
Xiuyun Meng ◽  
Jilu Liu
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Measurement Noise ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Loosely Coupled ◽  
Kalman Filter Algorithm ◽  
Adaptive Extended Kalman Filter

The Kalman Filter algorithm usually cannot estimate noise statistics in real-time, in order to deal with this issue, a new kind of improved Adaptive Extended Kalman Filter algorithm is proposed. Based on residual sequence, this algorithm mainly improves the adaptive estimator of the filter algorithm, which can estimate measurement noise in real-time. Furthermore, this new filter algorithm is applied to a SINS/GPS loosely-coupled integrated navigation system, which can automatically adjust the covariance matrix of measurement noise as noise varies in the system. Finally, the original Extended Kalman Filter and the improved Adaptive Extended Kalman Filter are applied respectively to simulate for the SINS/GPS loosely-coupled model. Tests demonstrate that, the improved Adaptive Extended Kalman Filter reduces both position error and velocity error compared with the original Extended Kalman Filter.  

Real-Time Signal Denoising Algorithm in Wheel Force Transducer

2014 ◽  
Vol 615 ◽  
pp. 244-247
Author(s):  
Dong Wang ◽  
Guo Yu Lin ◽  
Wei Gong Zhang
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Unscented Kalman Filter ◽  
Force Transducer ◽  
Time Signal ◽  
Signal Denoising ◽  
Observation Function ◽  
Simulation Results ◽  
Wheel Force Transducer

The wheel force transducer (WFT) is used to measure dynamic wheel loads. Unlike other force sensors, WFT is rotating with the wheel. For this reason, the outputs and the inputs of the transducer are nonlinearly related, and traditional Kalman Filter is not suitable. In this paper, a new real-time filter algorithm utilizing Quadrature Kalman Filter (QKF) is proposed to solve this problem. In Quadrature Kalman Filter, Singer model is introduced to track the wheel force, and the observation function is established for WFT. The simulation results illustrate that the new filter outperforms the traditional Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF).

scholarly journals Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1126
Author(s):  
Zhentao Hu ◽  
Linlin Yang ◽  
Yong Jin ◽  
Han Wang ◽  
Shibo Yang
Keyword(s):  
Real Time ◽  
Gaussian Mixture ◽  
Measurement Noise ◽  
Tracking Accuracy ◽  
Process Noise ◽  
Probability Hypothesis Density ◽  
Measurement Noise Covariance ◽  
Multi Target Tracking ◽  
Noise Covariance ◽  
Fading Factor

Assuming that the measurement and process noise covariances are known, the probability hypothesis density (PHD) filter is effective in real-time multi-target tracking; however, noise covariance is often unknown and time-varying for an actual scene. To solve this problem, a strong tracking PHD filter based on Variational Bayes (VB) approximation is proposed in this paper. The measurement noise covariance is described in the linear system by the inverse Wishart (IW) distribution. Then, the fading factor in the strong tracking principle uses the optimal measurement noise covariance at the previous moment to control the state prediction covariance in real-time. The Gaussian IW (GIW) joint distribution adopts the VB approximation to jointly return the measurement noise covariance and the target state covariance. The simulation results show that, compared with the traditional Gaussian mixture PHD (GM-PHD) and the VB-adaptive PHD, the proposed algorithm has higher tracking accuracy and stronger robustness in a more reasonable calculation time.

scholarly journals A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3809 ◽  
Author(s):  
Yushi Hao ◽  
Aigong Xu ◽  
Xin Sui ◽  
Yulei Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Measurement Noise ◽  
Model Errors ◽  
State Process ◽  
Stability And Accuracy ◽  
The Impact

Recently, the integration of an inertial navigation system (INS) and the Global Positioning System (GPS) with a two-antenna GPS receiver has been suggested to improve the stability and accuracy in harsh environments. As is well known, the statistics of state process noise and measurement noise are critical factors to avoid numerical problems and obtain stable and accurate estimates. In this paper, a modified extended Kalman filter (EKF) is proposed by properly adapting the statistics of state process and observation noises through the innovation-based adaptive estimation (IAE) method. The impact of innovation perturbation produced by measurement outliers is found to account for positive feedback and numerical issues. Measurement noise covariance is updated based on a remodification algorithm according to measurement reliability specifications. An experimental field test was performed to demonstrate the robustness of the proposed state estimation method against dynamic model errors and measurement outliers.

scholarly journals An Innovative Framework for Real Time Monitoring of Pollutant Point Sources in River Networks

2021 ◽  
Author(s):  
Mehdi Mazaheri ◽  
J. M. V. Samani ◽  
Fulvio Boano
Keyword(s):  
Real Time ◽  
Transport Model ◽  
River Network ◽  
Point Sources ◽  
Release Time ◽  
River Networks ◽  
Observation Data ◽  
Geostatistical Approach ◽  
Pollutant Sources ◽  
Simultaneous Identification

Abstract The simultaneous identification of location and source release history in complex river networks is a very complicated ill-posed problem, particularly in a case of multiple unknown pollutant sources with time-varying release pattern. This study presents an innovative method for simultaneous identification of the number, locations and release histories of multiple pollutant point sources in a river network using minimum observation data. Considering two different type of monitoring stations with an adaptive arrangement as well as real-time data collection at those stations and using a reliable numerical flow and transport model, at first the number and suspected reach of presence of pollutant sources are determined. Then the source location and its intensity function is calculated by solving inverse source problem using a geostatistical approach. A case study with three different scenarios in terms of the number, release time and location of pollutant sources are discussed, concerning a river network with unsteady and non-uniform flow. Results showed the capability of the proposed method in identifying of sought source characteristics even in complicated cases with simultaneous activity of multiple pollutant sources.

scholarly journals Performance Analysis of Computational Intelligence Correction

2021 ◽  
Author(s):  
Nalini Arasavali ◽  
Sasibhushanarao Gottapu
Keyword(s):  
Kalman Filter ◽  
Performance Analysis ◽  
Computational Intelligence ◽  
A Priori ◽  
Covariance Matrices ◽  
System Model ◽  
Process Noise ◽  
Navigation Algorithm ◽  
Modified Kalman Filter ◽  
Measurement Noises

Abstract Kalman filter (KF) is a widely used navigation algorithm, especially for precise positioning applications. However, the exact filter parameters must be defined a priori to use standard Kalman filters for coping with low error values. But for the dynamic system model, the covariance of process noise is a priori entirely undefined, which results in difficulties and challenges in the implementation of the conventional Kalman filter. Kalman Filter with recursive covariance estimation applied to solve those complicated functional issues, which can also be used in many other applications involving Kalaman filtering technology, a modified Kalman filter called MKF-RCE. While this is a better approach, KF with SAR tuned covariance has been proposed to resolve the problem of estimation for the dynamic model. The data collected at (x: 706970.9093 m, y: 6035941.0226 m, z: 1930009.5821 m) used to illustrate the performance analysis of KF with recursive covariance and KF with computational intelligence correction by means of SAR (Search and Rescue) tuned covariance, when the covariance matrices of process and measurement noises are completely unknown in advance.

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