Adaptive Cubature Kalman Filter Algorithm Based on Quaternion Error Modeling

2020 ◽  
Author(s):  
Kai Liu ◽  
You Zhao ◽  
Zhi-Gang Zhu
Keyword(s):  
Kalman Filter ◽  
Error Modeling ◽  
Cubature Kalman Filter ◽  
Kalman Filter Algorithm

scholarly journals Control Optimization of Stochastic Systems Based on Adaptive Correction CKF Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
FengJun Hu ◽  
Qian Zhang ◽  
Gang Wu
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Stochastic System ◽  
Stochastic Systems ◽  
System Control ◽  
Time Varying ◽  
Cubature Kalman Filter ◽  
Adaptive Correction ◽  
Kalman Filter Algorithm ◽  
Control Accuracy

Standard cubature Kalman filter (CKF) algorithm has some disadvantages in stochastic system control, such as low control accuracy and poor robustness. This paper proposes a stochastic system control method based on adaptive correction CKF algorithm. Firstly, a nonlinear time-varying discrete stochastic system model with stochastic disturbances is constructed. The control model is established by using the CKF algorithm, the covariance matrix of standard CKF is optimized by square root filter, the adaptive correction of error covariance matrix is realized by adding memory factor to the filter, and the disturbance factors in nonlinear time-varying discrete stochastic systems are eliminated by multistep feedback predictive control strategy, so as to improve the robustness of the algorithm. Simulation results show that the state estimation accuracy of the proposed adaptive cubature Kalman filter algorithm is better than that of the standard cubature Kalman filter algorithm, and the proposed adaptive correction CKF algorithm has good control accuracy and robustness in the UAV control test.

scholarly journals State estimation based on improved cubature Kalman filter algorithm

2020 ◽  
Vol 14 (5) ◽  
pp. 536-542 ◽  
Author(s):  
Jun Zhu ◽  
Bingchen Liu ◽  
Haixing Wang ◽  
Zihao Li ◽  
Zhe Zhang
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Cubature Kalman Filter ◽  
Kalman Filter Algorithm

An implementation of the cubature Kalman filter for estimating trajectory parameters and air data of a hypersonic vehicle

2019 ◽  
Vol 233 (12) ◽  
pp. 4554-4570
Author(s):  
Zhijian Ding ◽  
Huan Zhou ◽  
Feng Wang ◽  
Dongsheng Wu ◽  
Yingchuan Wu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Inertial Navigation System ◽  
Hypersonic Vehicle ◽  
Positioning System ◽  
System State ◽  
Sensing System ◽  
Cubature Kalman Filter ◽  
Global Positioning ◽  
Kalman Filter Algorithm

Trajectory parameters (including the position, velocity, and attitude angles of a vehicle) and air data (consisting of the flow angles, the Mach number, and the freestream static pressure) are vital data for the analysis and evaluation process in the hypersonic flight tests. This paper describes a data fusion estimation algorithm for a flush air data sensing system/inertial navigation system/global positioning system integrated system, which is used to estimate the trajectory parameters and air data for an unpowered hypersonic vehicle. In the approach, the raw outputs of flush air data sensing system (i.e. the surface pressure measurements) are integrated with global positioning system results (the vehicle’s position and velocity) and inertial navigation system measurements (including the acceleration and the angular velocity measurements) by using a nonlinear Kalman filter algorithm. Firstly, the system state vector is defined with the trajectory parameters, the biases of the inertial sensors and the winds. Then, the system dynamic models are built based on the motion equations of an unpowered hypersonic vehicle, the inertial sensor error models and the wind model. Besides, the system measurement vector is designed with the global positioning system results and the flush air data sensing system raw outputs. Based on these works, the system state is directly estimated by using the cubature Kalman filter algorithm. After that, the air data is calculated based on the estimated values and a high-fidelity model of atmosphere. Simulation cases are implemented to assess the performance of the proposed algorithm. The results show that the proposed method could estimate the trajectory parameters and air data for hypersonic vehicle with a high precision.

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