scholarly journals Application of Improved Simplex Quadrature Cubature Kalman Filter in Nonlinear Dynamic System

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Ting Cao ◽  
Huo-tao Gao ◽  
Chun-feng Sun ◽  
Yun Ling ◽  
Guo-bao Ru
Keyword(s):  
Kalman Filter ◽  
Dynamic System ◽  
Nonlinear Dynamic ◽  
Nonlinear Dynamic System ◽  
Quadrature Rule ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Weighted Integral ◽  
Spherical Simplex

A novel spherical simplex Gauss–Laguerre quadrature cubature Kalman filter is proposed to improve the estimation accuracy of nonlinear dynamic system. The nonlinear Gaussian weighted integral has been approximately evaluated using the spherical simplex rule and the arbitrary order Gauss–Laguerre quadrature rule. Thus, a spherical simplex Gauss–Laguerre cubature quadrature rule is developed, from which the general computing method of the simplex cubature quadrature points and the corresponding weights are obtained. Then, under the nonlinear Kalman filtering framework, the spherical simplex Gauss–Laguerre quadrature cubature Kalman filter is derived. A high-dimensional nonlinear state estimation problem and a target tracking problem are utilized to demonstrate the effectiveness of the proposed spherical simplex Gauss–Laguerre cubature quadrature rule to improve the performance.

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 Calibration and Improvement of an Odometry Model with Dynamic Wheel and Lateral Dynamics Integration

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 337
Author(s):  
Máté Fazekas ◽  
Péter Gáspár ◽  
Balázs Németh
Keyword(s):  
Kalman Filter ◽  
Measurement Uncertainty ◽  
Nonlinear Dynamic ◽  
Autonomous System ◽  
Nonlinear Dynamic System ◽  
Cost Effective ◽  
Estimation Accuracy ◽  
Localization Accuracy ◽  
Lateral Dynamics ◽  
Effective System

Localization is a key part of an autonomous system, such as a self-driving car. The main sensor for the task is the GNSS, however its limitations can be eliminated only by integrating other methods, for example wheel odometry, which requires a well-calibrated model. This paper proposes a novel wheel odometry model and its calibration. The parameters of the nonlinear dynamic system are estimated with Gauss–Newton regression. Due to only automotive-grade sensors are applied to reach a cost-effective system, the measurement uncertainty highly corrupts the estimation accuracy. The problem is handled with a unique Kalman-filter addition to the iterative loop. The experimental results illustrate that without the proposed improvements, in particular the dynamic wheel assumption and integrated filtering, the model cannot be calibrated precisely. With the well-calibrated odometry, the localization accuracy improves significantly and the system can be used as a cost-effective motion estimation sensor in autonomous functions.

scholarly journals Spherical Simplex-Radial Cubature Quadrature Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Zhaoming Li ◽  
Wenge Yang
Keyword(s):  
Kalman Filter ◽  
Nonlinear Filtering ◽  
Arbitrary Order ◽  
Nonlinear Function ◽  
Quadrature Rule ◽  
Specific Form ◽  
Bayesian Filtering ◽  
The Novel ◽  
Weighted Integral ◽  
Spherical Simplex

A spherical simplex-radial cubature quadrature Kalman filter (SSRCQKF) is proposed in order to further improve the nonlinear filtering accuracy. The Gaussian probability weighted integral of the nonlinear function is decomposed into spherical integral and radial integral, which are approximated by spherical simplex cubature rule and arbitrary order Gauss-Laguerre quadrature rule, respectively, and the novel spherical simplex-radial cubature quadrature rule is obtained. Combined with the Bayesian filtering framework, the general form and the specific form of SSRCQKF are put forward, and the numerical simulation results indicate that the proposed algorithm can achieve a higher filtering accuracy than CKF and SSRCKF.

Diffusion nonlinear Kalman filter with intermittent observations

2017 ◽  
Vol 232 (15) ◽  
pp. 2775-2783 ◽  
Author(s):  
Guoqing Wang ◽  
Ning Li ◽  
Yonggang Zhang
Keyword(s):  
Kalman Filter ◽  
Sensor Networks ◽  
State Estimation ◽  
Target Tracking ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Information Filter ◽  
Cubature Kalman Filter ◽  
Nonlinear Kalman Filter ◽  
Nonlinear State

In this article, we consider the distributed nonlinear state estimation over sensor networks under the diffusion Kalman filter paradigm, where data only exchanges among the neighbourhoods of sensors. We first obtain a novel nonlinear Kalman filter with intermittent observations based on cubature Kalman filter. After that, its equivalent information filter is derived, and the proposed diffusion cubature Kalman filter with intermittent observations is designed based on this information filter. The effectiveness of proposed algorithms is demonstrated by a typical target tracking example, and our algorithm has similar estimation accuracy when comparing with existing algorithms while consuming less computation and communication resources.

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