scholarly journals Design of an Interacting Multiple Model-Cubature Kalman Filter Approach for Vehicle Sideslip Angle and Tire Forces Estimation

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
SuoJun Hou ◽  
Wenbo Xu ◽  
Gang Liu
Keyword(s):  
Kalman Filter ◽  
Estimation Method ◽  
Lateral Acceleration ◽  
Accurate Estimation ◽  
Interacting Multiple Model ◽  
Tire Model ◽  
Multiple Model ◽  
Vehicle Model ◽  
Sideslip Angle ◽  
Cubature Kalman Filter

Vehicle states estimation (e.g., vehicle sideslip angle and tire force) is a key factor for vehicle stability control. However, the accurate values of these parameters could not be obtained directly. In this paper, an interacting multiple model-cubature Kalman filter (IMM-CKF) is used to estimate the vehicle state parameters. And improvements about estimation method are achieved in this paper. Firstly, the accuracy of the reference model is improved by building two different models: one is 7-degree-of-freedom (7 DOF) vehicle model with linear tire model, and the other is 7 DOF vehicle model with nonlinear Dugoff tire model. Secondly, the different models are switched by IMM-CKF to match different driving condition. Thirdly, the lateral acceleration correction for sideslip angle estimation is considered, because the sensor of lateral acceleration is easy to be influenced by the gravity on banked road. Then, to compare cubature Kalman filter (CKF) estimation method and IMM-CKF estimation method Hardware-In-Loop (HIL) tests are carried out in the paper. And simulation results show that IMM-CKF methodology can provide accurate estimation values of vehicle states parameters.

scholarly journals Vehicle Sideslip Angle Estimation Based on Hybrid Kalman Filter

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jing Li ◽  
Jiaxu Zhang
Keyword(s):  
Kalman Filter ◽  
Control Systems ◽  
Interacting Multiple Model ◽  
Square Root ◽  
Tire Model ◽  
Multiple Model ◽  
Sideslip Angle ◽  
Safety Control ◽  
Cubature Kalman Filter ◽  
Vehicle Dynamic Model

Vehicle sideslip angle is essential for active safety control systems. This paper presents a new hybrid Kalman filter to estimate vehicle sideslip angle based on the 3-DoF nonlinear vehicle dynamic model combined with Magic Formula tire model. The hybrid Kalman filter is realized by combining square-root cubature Kalman filter (SCKF), which has quick convergence and numerical stability, with square-root cubature based receding horizon Kalman FIR filter (SCRHKF), which has robustness against model uncertainty and temporary noise. Moreover, SCKF and SCRHKF work in parallel, and the estimation outputs of two filters are merged by interacting multiple model (IMM) approach. Experimental results show the accuracy and robustness of the hybrid Kalman filter.

Explicit Fractional Model Order Estimation Using Unscented and Ensemble Kalman Filters

2011 ◽  
Author(s):  
Michailas Romanovas ◽  
Lasse Klingbeil ◽  
Martin Traechtler ◽  
Yiannos Manoli
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Computationally Efficient ◽  
Ensemble Kalman Filters ◽  
Model Order ◽  
Order Estimation ◽  
Augmented State

The article presents an approach for combining methods of recursive Bayesian estimation with models of dynamical systems with varying differentiation order. The work addresses the problem of explicit fractional order estimation and tracking by constructing an efficient Unscented Kalman filter, where the model order is directly estimated within an augmented state along with the variables of interest. The feasibility of the estimation method is assessed using a benchmark problem based on a simplified fractional neuron firing rate model and time-dependent differentiation order. The proposed technique is compared to an implicit method based on Interacting Multiple Model filtering and a computationally efficient method using a modification of the Ensemble Kalman filter. The performance with respect to different parameters and filter settings is analyzed and a corresponding discussion is provided.

scholarly journals Sideslip Angle Fusion Estimation Method of an Autonomous Electric Vehicle Based on Robust Cubature Kalman Filter with Redundant Measurement Information

2019 ◽  
Vol 10 (2) ◽  
pp. 34
Author(s):  
Te Chen ◽  
Long Chen ◽  
Xing Xu ◽  
Yingfeng Cai ◽  
Haobin Jiang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Autonomous Vehicle ◽  
Estimation Method ◽  
Coupling Model ◽  
Wheel Speed ◽  
Measurement Information ◽  
Sideslip Angle ◽  
Safety Control ◽  
Coupling Relationship ◽  
Cubature Kalman Filter

Accurate and reliable estimation information of sideslip angle is very important for intelligent motion control and active safety control of an autonomous vehicle. To solve the problem of sideslip angle estimation of an autonomous vehicle, a sideslip angle fusion estimation method based on robust cubature Kalman filter and wheel-speed coupling relationship is proposed in this paper. The vehicle dynamics model, tire model, and wheel speed coupling model are established and discretized, and a robust cubature Kalman filter is designed for vehicle running state estimation according to the discrete vehicle model. An adaptive measurement-update solution of the robust cubature Kalman filter is presented to improve the robustness of estimation, and then, the wheel-speed coupling relationship is introduced to the measurement update equation of the robust cubature Kalman filter and an adaptive sideslip angle fusion estimation method is designed. The simulations in the CarSim-Simulink co-simulation platform and the actual vehicle road test are carried out, and the effectiveness of the proposed estimation method is validated by corresponding comparative analysis results.

Maneuvering Target Tracking Algorithm Based on IMMSCKF

2013 ◽  
Vol 419 ◽  
pp. 145-150
Author(s):  
Jian Wang Hu ◽  
Peng Zhou ◽  
Hao Xie ◽  
Le Luo ◽  
Hou Bo He
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Interacting Multiple Model ◽  
Weighted Sum ◽  
Square Root ◽  
Multiple Model ◽  
Maneuvering Target Tracking ◽  
Switching Speed ◽  
Maneuvering Target ◽  
Cubature Kalman Filter

Aiming at the tracking filters are liable to diverge and the tracking precision is low when tracking nonlinear maneuvering target, an Interacting Multiple Model Square-root Cubature Kalman Filter (IMMSCKF) is developed by introducing Square-root Cubature Kalman Filter (SCKF) into Interacting Multiple Model (IMM). This method uses SCKF for filtering each model, the weighted sum of the outputs of all parallel SCKF is taken as the output of IMMSCKF. Simulation shows that IMMSCKF has higher precision, quicker model switching speed, and smaller calculation cost compared with IMMUKF.

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