Vehicle State Estimation Using Cubature Kalman Filter

2014 ◽  
Author(s):  
Xiaoshuai Xin ◽  
Jinxi Chen ◽  
Jianxiao Zou
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Cubature Kalman Filter ◽  
Vehicle State Estimation

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 A Novel Comprehensive Scheme for Vehicle State Estimation Using Dual Extended H-Infinity Kalman Filter

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1526
Author(s):  
Fengjiao Zhang ◽  
Yan Wang ◽  
Jingyu Hu ◽  
Guodong Yin ◽  
Song Chen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Estimation Accuracy ◽  
Vehicle Speed ◽  
Sideslip Angle ◽  
H Infinity ◽  
Yaw Rate ◽  
Cost Constraints ◽  
Active Safety Systems ◽  
Vehicle State Estimation

The performance of vehicle active safety systems relies on accurate vehicle state information. Estimation of vehicle state based on onboard sensors has been popular in research due to technical and cost constraints. Although many experts and scholars have made a lot of research efforts for vehicle state estimation, studies that simultaneously consider the effects of noise uncertainty and model parameter perturbation have rarely been reported. In this paper, a comprehensive scheme using dual Extended H-infinity Kalman Filter (EH∞KF) is proposed to estimate vehicle speed, yaw rate, and sideslip angle. A three-degree-of-freedom vehicle dynamics model is first established. Based on the model, the first EH∞KF estimator is used to identify the mass of the vehicle. Simultaneously, the second EH∞KF estimator uses the result of the first estimator to predict the vehicle speed, yaw rate, and sideslip angle. Finally, simulation tests are carried out to demonstrate the effectiveness of the proposed method. The test results indicate that the proposed method has higher estimation accuracy than the extended Kalman filter.

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.

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 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
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