scholarly journals Interacting Multiple Model UAV Navigation Algorithm Based on a Robust Cubature Kalman Filter

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 81034-81044
Author(s):  
Xuhang Liu ◽  
Xiaoxiong Liu ◽  
Weiguo Zhang ◽  
Yue Yang
Keyword(s):  
Kalman Filter ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Navigation Algorithm ◽  
Cubature Kalman Filter ◽  
Uav Navigation

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.

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.

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 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.

Spherical Simplex Unscented Kalman Filter-Based Jumping and Static Interacting Multiple Model

2017 ◽  
Vol 32 (04) ◽  
pp. 1850010
Author(s):  
Yi Pan ◽  
Hui Ye ◽  
Keke He
Keyword(s):  
Kalman Filter ◽  
Nonlinear Filtering ◽  
System Parameter ◽  
Unscented Kalman Filter ◽  
Continuous System ◽  
The State ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Real Time System ◽  
Spherical Simplex

A modified interacting multiple model (IMM) method called spherical simplex unscented Kalman filter-based jumping and static IMM (SSUKF-JSIMM) is proposed to solve the problem of nonlinear filtering with unknown continuous system parameter. SSUKF-JSIMM regards the continuous system parameter space as a union of disjoint regions, and each region is assigned to a model. For each model, under the assumption that the parameter belongs to the corresponding region, one sub-filter is used to estimate the parameter and the state when the parameter is presumed to be jumping, and another sub-filter is used to estimate the parameter and the state when the parameter is presumed to be static. Considering that spherical simplex unscented Kalman filter (SSUKF) is more suitable for a real-time system than the unscented Kalman filter (UKF), SSUKFs are adopted as the sub-filters of SSUKF-JSIMM. Results of the two SSUKFs are fused as the estimation output of the model. Experimental results show that SSUKF-JSIMM achieves higher performance than IMM, SIR, and UKF in bearings-only tracking problem.

Sign in / Sign up

Export Citation Format

Share Document