An adaptive cubature Kalman filter algorithm for inertial and land-based navigation system

2016 ◽  
Vol 51 ◽  
pp. 52-60 ◽  
Author(s):  
Min Liu ◽  
Jizhou Lai ◽  
Zhimin Li ◽  
Jianye Liu
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Cubature Kalman Filter ◽  
Kalman Filter Algorithm

scholarly journals Robust Adaptive Cubature Kalman Filter and Its Application to Ultra-Tightly Coupled SINS/GPS Navigation System

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2352 ◽  
Author(s):  
Xin Zhao ◽  
Jianli Li ◽  
Xunliang Yan ◽  
Shaowen Ji
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Kinematic Model ◽  
Tracking Performance ◽  
Typical Application ◽  
Gross Error ◽  
Cubature Kalman Filter ◽  
Tightly Coupled ◽  
Robust Adaptive ◽  
High Dynamics

In this paper, we propose a robust adaptive cubature Kalman filter (CKF) to deal with the problem of an inaccurately known system model and noise statistics. In order to overcome the kinematic model error, we introduce an adaptive factor to adjust the covariance matrix of state prediction, and process the influence introduced by dynamic disturbance error. Aiming at overcoming the abnormality error, we propose the robust estimation theory to adjust the CKF algorithm online. The proposed adaptive CKF can detect the degree of gross error and subsequently process it, so the influence produced by the abnormality error can be solved. The paper also studies a typical application system for the proposed method, which is the ultra-tightly coupled navigation system of a hypersonic vehicle. Highly dynamical scene experimental results show that the proposed method can effectively process errors aroused by the abnormality data and inaccurate model, and has better tracking performance than UKF and CKF tracking methods. Simultaneously, the proposed method is superior to the tracing method based on a single-modulating loop in the tracking performance. Thus, the stable and high-precision tracking for GPS satellite signals are preferably achieved and the applicability of the system is promoted under the circumstance of high dynamics and weak signals. The effectiveness of the proposed method is verified by a highly dynamical scene experiment.

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
Sign in / Sign up

Export Citation Format

Share Document