scholarly journals Double-Layer Cubature Kalman Filter for Nonlinear Estimation

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 986 ◽  
Author(s):  
Feng Yang ◽  
Yujuan Luo ◽  
Litao Zheng
Keyword(s):  
Kalman Filter ◽  
Computational Complexity ◽  
Double Layer ◽  
Poor Performance ◽  
The State ◽  
Estimation Accuracy ◽  
Sampling Point ◽  
Stochastic Sampling ◽  
Deterministic Sampling ◽  
Cubature Kalman Filter

The cubature Kalman filter (CKF) has poor performance in strongly nonlinear systems while the cubature particle filter has high computational complexity induced by stochastic sampling. To address these problems, a novel CKF named double-Layer cubature Kalman filter (DLCKF) is proposed. In the proposed DLCKF, the prior distribution is represented by a set of weighted deterministic sampling points, and each deterministic sampling point is updated by the inner CKF. Finally, the update mechanism of the outer CKF is used to obtain the state estimations. Simulation results show that the proposed algorithm has not only high estimation accuracy but also low computational complexity, compared with the state-of-the-art filtering algorithms.

scholarly journals A Strong Tracking Mixed-Degree Cubature Kalman Filter Method and Its Application in a Quadruped Robot

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2251 ◽  
Author(s):  
Jikai Liu ◽  
Pengfei Wang ◽  
Fusheng Zha ◽  
Wei Guo ◽  
Zhenyu Jiang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Real Time ◽  
Quadruped Robot ◽  
The State ◽  
Estimation Accuracy ◽  
Robot System ◽  
Calculation Time ◽  
Motion State ◽  
Cubature Kalman Filter

The motion state of a quadruped robot in operation changes constantly. Due to the drift caused by the accumulative error, the function of the inertial measurement unit (IMU) will be limited. Even though multi-sensor fusion technology is adopted, the quadruped robot will lose its ability to respond to state changes after a while because the gain tends to be constant. To solve this problem, this paper proposes a strong tracking mixed-degree cubature Kalman filter (STMCKF) method. According to system characteristics of the quadruped robot, this method makes fusion estimation of forward kinematics and IMU track. The combination mode of traditional strong tracking cubature Kalman filter (TSTCKF) and strong tracking is improved through demonstration. A new method for calculating fading factor matrix is proposed, which reduces sampling times from three to one, saving significantly calculation time. At the same time, the state estimation accuracy is improved from the third-degree accuracy of Taylor series expansion to fifth-degree accuracy. The proposed algorithm can automatically switch the working mode according to real-time supervision of the motion state and greatly improve the state estimation performance of quadruped robot system, exhibiting strong robustness and excellent real-time performance. Finally, a comparative study of STMCKF and the extended Kalman filter (EKF) that is commonly used in quadruped robot system is carried out. Results show that the method of STMCKF has high estimation accuracy and reliable ability to cope with sudden changes, without significantly increasing the calculation time, indicating the correctness of the algorithm and its great application value in quadruped robot system.

Developing Computationally Efficient Nonlinear Cubature Kalman Filtering for Visual Inertial Odometry

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Trung Nguyen ◽  
George K. I. Mann ◽  
Andrew Vardy ◽  
Raymond G. Gosine
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
High Speed ◽  
Three Dimensional ◽  
Measurement Model ◽  
Estimation Accuracy ◽  
Computationally Efficient ◽  
Fusion Algorithm ◽  
Deterministic Sampling ◽  
Cubature Kalman Filter

This paper presents a computationally efficient sensor-fusion algorithm for visual inertial odometry (VIO). The paper utilizes trifocal tensor geometry (TTG) for visual measurement model and a nonlinear deterministic-sampling-based filter known as cubature Kalman filter (CKF) to handle the system nonlinearity. The TTG-based approach is developed to replace the computationally expensive three-dimensional-feature-point reconstruction in the conventional VIO system. This replacement has simplified the system architecture and reduced the processing time significantly. The CKF is formulated for the VIO problem, which helps to achieve a better estimation accuracy and robust performance than the conventional extended Kalman filter (EKF). This paper also addresses the computationally efficient issue associated with Kalman filtering structure using cubature information filter (CIF), the CKF version on information domain. The CIF execution avoids the inverse computation of the high-dimensional innovation covariance matrix, which in turn further improves the computational efficiency of the VIO system. Several experiments use the publicly available datasets for validation and comparing against many other VIO algorithms available in the recent literature. Overall, this proposed algorithm can be implemented as a fast VIO solution for high-speed autonomous robotic systems.

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 Mixed-Degree Spherical Simplex-Radial Cubature Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Shiyuan Wang ◽  
Yali Feng ◽  
Shukai Duan ◽  
Lidan Wang
Keyword(s):  
Kalman Filter ◽  
Computational Complexity ◽  
Kalman Filters ◽  
Order Moment ◽  
Low Degree ◽  
Cubature Kalman Filter ◽  
Highly Nonlinear ◽  
Spherical Simplex ◽  
The Cost ◽  
High Degree

Conventional low degree spherical simplex-radial cubature Kalman filters often generate low filtering accuracy or even diverge for handling highly nonlinear systems. The high-degree Kalman filters can improve filtering accuracy at the cost of increasing computational complexity; nevertheless their stability will be influenced by the negative weights existing in the high-dimensional systems. To efficiently improve filtering accuracy and stability, a novel mixed-degree spherical simplex-radial cubature Kalman filter (MSSRCKF) is proposed in this paper. The accuracy analysis shows that the true posterior mean and covariance calculated by the proposed MSSRCKF can agree accurately with the third-order moment and the second-order moment, respectively. Simulation results show that, in comparison with the conventional spherical simplex-radial cubature Kalman filters that are based on the same degrees, the proposed MSSRCKF can perform superior results from the aspects of filtering accuracy and computational complexity.

Iterated Cubature Kalman Filter for State Estimation of Maneuver Reentry Vehicle

2012 ◽  
Vol 466-467 ◽  
pp. 1329-1333
Author(s):  
Jing Mu ◽  
Chang Yuan Wang
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
High Accuracy ◽  
Estimation Accuracy ◽  
Reentry Vehicle ◽  
Vehicle Simulation ◽  
Cubature Kalman Filter ◽  
Simulation Results ◽  
Iterate Process

We present the new filters named iterated cubature Kalman filter (ICKF). The ICKF is implemented easily and involves the iterate process for fully exploiting the latest measurement in the measurement update so as to achieve the high accuracy of state estimation We apply the ICKF to state estimation for maneuver reentry vehicle. Simulation results indicate ICKF outperforms over the unscented Kalman filter and square root cubature Kalman filter in state estimation accuracy.

scholarly journals State of Charge Estimation for Lithium-Ion Battery by Using Dual Square Root Cubature Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Luping Chen ◽  
Liangjun Xu ◽  
Ruoyu Wang
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Square Root ◽  
Battery Management ◽  
Balance Performance ◽  
Soc Estimation ◽  
Cubature Kalman Filter

The state of charge (SOC) plays an important role in battery management systems (BMS). However, SOC cannot be measured directly and an accurate state estimation is difficult to obtain due to the nonlinear battery characteristics. In this paper, a method of SOC estimation with parameter updating by using the dual square root cubature Kalman filter (DSRCKF) is proposed. The proposed method has been validated experimentally and the results are compared with dual extended Kalman filter (DEKF) and dual square root unscented Kalman filter (DSRUKF) methods. Experimental results have shown that the proposed method has the most balance performance among them in terms of the SOC estimation accuracy, execution time, and convergence rate.

Multiple Robust High-degree Cubature Kalman Filter for Relative Position and Attitude Estimation of Satellite Formation

2019 ◽  
Vol 72 (5) ◽  
pp. 1254-1274 ◽  
Author(s):  
Ning Li ◽  
Wentao Ma ◽  
Weishi Man ◽  
Liu Cao ◽  
Hui Zhang
Keyword(s):  
Kalman Filter ◽  
State Space ◽  
High Dimension ◽  
Relative Position ◽  
Attitude Estimation ◽  
The State ◽  
Robust Performance ◽  
Cubature Kalman Filter ◽  
Estimation System ◽  
High Degree

The High-degree Cubature Kalman Filter (HCKF) is proposed as a novel methodology based on the arbitrary degree spherical rule, which can achieve better performance than the traditional Kalman filter. However, it also has a large calculation burden when used in a high-dimension and high-degree of accuracy estimation system. The number of sampling points of an HCKF increases polynomially with increasing state-space dimensions, which further increases the calculation burden. The reduction of the number of the state-space dimensions is the main contribution of this study. A strategy for HCKF based on the partitioning of the state-space and orthogonal principle is introduced, referred to as the Multiple Robust HCKF (MRHCKF). It is shown that this technique can effectively reduce the calculation burden for the high-dimension system with robust performance. Numerical simulations are performed for the example of high-dimension relative position and attitude estimation to show that the proposed method can obtain nearly the same performance as the HCKF, while drastically reducing computational complexity.

Optimal control of the digital twin of the enterprise.

2021 ◽  
Vol 5 ◽  
Author(s):  
S.N. Masaev ◽  
Keyword(s):  
Optimal Control ◽  
Kalman Filter ◽  
Computational Complexity ◽  
Execution Time ◽  
The State ◽  
Control Methods ◽  
Optimal Ratio ◽  
Digital Twin ◽  
The Past ◽  
Production Processes

The enterprise is presented as a digital twin of interrelated production processes, support processes and administrative processes. A semantic indicator for a general assessment of the state of the system at different points in time is given. The semantic indicator is applied to assess the optimal control by Kalman filter. The obtained estimates of optimal control make it possible to take into account the resources consumed by the processes in the past periods. The obtained optimal control can be used with other control methods through control and observation matrices. Estimates of the optimal ratio of values are obtained: the amount of the resource in the system, the method of attracting the resource, the execution time of the method, the computational complexity of the method.

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