The high-degree cubature Kalman filter

2012 ◽  
Author(s):  
Bin Jia ◽  
Ming Xin ◽  
Yang Cheng
Keyword(s):  
Kalman Filter ◽  
Cubature Kalman Filter ◽  
High Degree

scholarly journals Moving State Marine SINS Initial Alignment Based on High Degree CKF

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yong-Gang Zhang ◽  
Yu-Long Huang ◽  
Zhe-Min Wu ◽  
Ning Li
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Alignment Method ◽  
Misalignment Angle ◽  
Initial Alignment ◽  
Central Difference ◽  
Angle Condition ◽  
Cubature Kalman Filter ◽  
High Degree ◽  
Central Difference Kalman Filter

A new moving state marine initial alignment method of strap-down inertial navigation system (SINS) is proposed based on high-degree cubature Kalman filter (CKF), which can capture higher order Taylor expansion terms of nonlinear alignment model than the existing third-degree CKF, unscented Kalman filter and central difference Kalman filter, and improve the accuracy of initial alignment under large heading misalignment angle condition. Simulation results show the efficiency and advantage of the proposed initial alignment method as compared with existing initial alignment methods for the moving state SINS initial alignment with large heading misalignment angle.

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.

scholarly journals Quadrotor UAV state estimation based on High-Degree Cubature Kalman filter

2016 ◽  
Vol 49 (17) ◽  
pp. 349-354 ◽  
Author(s):  
Hamza Benzerrouk ◽  
Alexander Nebylov ◽  
Hassen Salhi
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Quadrotor Uav ◽  
Cubature Kalman Filter ◽  
High Degree

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.

scholarly journals Application of MCC-based Robust High-degree Cubature Kalman Filter in Integrated Navigation System

2019 ◽  
Vol 1168 (6) ◽  
pp. 062029
Author(s):  
Ming-qi Xu ◽  
Guo-rong Huang ◽  
Hang Lu ◽  
Zhi-ying Peng ◽  
Shun-yi Hao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Cubature Kalman Filter ◽  
High Degree
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