scholarly journals Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter

2021 ◽  
Vol 11 (22) ◽  
pp. 10772
Author(s):  
Wan Wenkang ◽  
Feng Jingan ◽  
Song Bao ◽  
Li Xinxin
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Cooperative Control ◽  
Operating Conditions ◽  
Interacting Multiple Model ◽  
Square Root ◽  
Multiple Model ◽  
Cubature Kalman Filter ◽  
Vehicle State Estimation ◽  
Three Degree Of Freedom

The distributed drive arrangement form has better potential for cooperative control of dynamics, but this drive arrangement form increases the parameter acquisition workload of the control system and increases the difficulty of vehicle control accordingly. In order to observe the vehicle motion state accurately and in real-time, while reducing the effect of uncertainty in noise statistical information, the vehicle state observer is designed based on interacting multiple model theory with square root cubature Kalman filter (IMM-SCKF). The IMM-SCKF algorithm sub-model considers different state noise and measurement noise, and the introduction of the square root filter reduces the complexity of the algorithm while ensuring accuracy and real-time performance. To estimate the vehicle longitudinal, lateral, and yaw motion states, the algorithm uses a three degree of freedom (3-DOF) vehicle dynamics model and a nonlinear brush tire model, which is then validated in a Carsim-Simulink co-simulation platform for multiple operating conditions. The results show that the IMM-SCKF algorithm’s fusion output results can effectively follow the sub-model with smaller output errors, and that the IMM-SCKF algorithm’s results are superior to the traditional SCKF algorithm’s results.

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.

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.

scholarly journals A Variational Bayesian and Huber-Based Robust Square Root Cubature Kalman Filter for Lithium-Ion Battery State of Charge Estimation

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1717 ◽  
Author(s):  
Jing Hou ◽  
He He ◽  
Yan Yang ◽  
Tian Gao ◽  
Yifan Zhang
Keyword(s):  
Kalman Filter ◽  
Lithium Ion ◽  
Operating Conditions ◽  
State Of Charge ◽  
Constant Current ◽  
Square Root ◽  
Variational Bayesian ◽  
Soc Estimation ◽  
Cubature Kalman Filter ◽  
M Estimation

An accurate state of charge (SOC) estimation is vital for safe operation and efficient management of lithium-ion batteries. To improve the accuracy and robustness, an adaptive and robust square root cubature Kalman filter based on variational Bayesian approximation and Huber’s M-estimation (VB-HASRCKF) is proposed. The variational Bayesian (VB) approximation is used to improve the adaptivity by simultaneously estimating the measurement noise covariance and the SOC, while Huber’s M-estimation is employed to enhance the robustness with respect to the outliers in current and voltage measurements caused by adverse operating conditions. A constant-current discharge test and an urban dynamometer driving schedule (UDDS) test are performed to verify the effectiveness and superiority of the proposed algorithm by comparison with the square root cubature Kalman filter (SRCKF), the VB-based SRCKF, and the Huber-based SRCKF. The experimental results show that the proposed VB-HASRCKF algorithm outperforms the other three filters in terms of SOC estimation accuracy and robustness, with a little higher computation complexity.

scholarly journals State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1124
Author(s):  
Wasiq Ali ◽  
Yaan Li ◽  
Muhammad Asif Zahoor Raja ◽  
Wasim Ullah Khan ◽  
Yigang He
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Kalman Filter ◽  
State Estimation ◽  
Real Time ◽  
Interacting Multiple Model ◽  
Bayesian Filtering ◽  
Multiple Model ◽  
Neural Computing ◽  
Narx Neural Network

In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object. The designed intelligent strategy is exploiting the strength of nonlinear autoregressive with an exogenous input (NARX) network model, which has the capability for estimating the dynamics of the systems that follow the discrete-time Markov chain. Nonlinear Bayesian filtering techniques are often applied for underwater maneuvering state estimation applications by following state-space methodology. The robustness and precision of NARX neural network are efficiently investigated for accurate state prediction of the passive Markov chain highly maneuvering underwater target. A continuous coordinated turning trajectory of an underwater maneuvering object is modeled for analyzing the performance of the neural computing paradigm. State estimation modeling is developed in the context of bearings only tracking technology in which the efficiency of the NARX neural network is investigated for ideal and complex ocean environments. Real-time position and velocity of maneuvering object are computed for five different cases by varying standard deviations of white Gaussian measured noise. Sufficient Monte Carlo simulation results validate the competence of NARX neural computing over conventional generalized pseudo-Bayesian filtering algorithms like an interacting multiple model extended Kalman filter and an interacting multiple model unscented Kalman filter.

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.

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