Enhanced Full-State Estimation and Dynamic-Model-Based Prediction for Road-Vehicles

2014 ◽  
Author(s):  
Aliakbar Alamdari ◽  
Javad Sovizi ◽  
Venkat N. Krovi
Keyword(s):  
State Estimation ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Nonlinear Differential Equations ◽  
Predictive Performance ◽  
Stability Control ◽  
High Fidelity ◽  
Electronic Stability Program ◽  
Uneven Terrain ◽  
Safety Systems

In this paper, we address the enhanced state estimation and prediction system for automobile applications by fusing relatively low-cost and noisy Inertial Navigation System (INS) sensing with Global Positioning System (GPS) measurements. An unscented Kalman filter is used to merge multi-rate measurements from GPS and INS sensors together with a high-fidelity vehicle-dynamics model for state-predictions. The high-fidelity motion model (including suspension-effects) for the vehicle motion trajectory on uneven terrain is captured by a 20-state system of nonlinear differential equations. Computer simulation results illustrate the effectiveness of sensor-fusion (building upon the merger of an inexpensive INS sensing with GPS based measurements) to accurately estimate the full system-state. The relative ease of implementation, accuracy and predictive performance with low-cost sensing will facilitate its use in various electronic control and safety-systems, such as Electronic Stability Program, Anti-lock Brake Systems, and the Lateral Dynamic Stability Control.

scholarly journals Vehicle Sideslip Angle Estimation Using Two Single-Antenna GPS Receivers

2010 ◽  
Author(s):  
Jong-Hwa Yoon ◽  
Huei Peng
Keyword(s):  
Low Cost ◽  
Longitudinal Velocity ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Sideslip Angle ◽  
Gps Receivers ◽  
Single Antenna ◽  
Active Safety Systems ◽  
Safety Systems

Knowing vehicle sideslip angle accurately is critical for active safety systems such as Electronic Stability Control (ESC). Vehicle sideslip angle can be measured through optical speed sensors, or dual-antenna GPS. These measurement systems are costly (∼$5k to $100k), which prohibits wide adoption of such systems. This paper demonstrates that the vehicle sideslip angle can be estimated in real-time by using two low-cost single-antenna GPS receivers. Fast sampled signals from an Inertial Measurement Unit (IMU) compensate for the slow update rate of the GPS receivers through an Extended Kalman Filter (EKF). Bias errors of the IMU measurements are estimated through an EKF to improve the sideslip estimation accuracy. A key challenge of the proposed method lies in the synchronization of the two GPS receivers, which is achieved through an extrapolated update method. Analysis reveals that the estimation accuracy of the proposed method relies mainly on vehicle yaw rate and longitudinal velocity. Experimental results confirm the feasibility of the proposed method.

scholarly journals A Modified Unscented Kalman Filter Combined with Ant Lion Optimization for Vehicle State Estimation

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Yixi Zhang ◽  
Jian Ma ◽  
Xuan Zhao ◽  
Xiaodong Liu ◽  
Kai Zhang
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
The Road ◽  
Experiment Platform ◽  
Ant Lion Optimization ◽  
Ant Lion

Accurate estimation of vehicle states is extremely crucial for vehicle stability control. As a reliable estimation methodology, the unscented Kalman filter (UKF) has been widely utilized in vehicle control. However, the estimation accuracy still needs to be improved caused by the unpredictable measurement and process noise. In this paper, a novel modified UKF state estimation methodology combined with the ant lion optimization (ALO) is proposed for the stability control of a four in-wheel motor independent drive electric vehicle (4WIDEV). First, the optimal performance of the ALO algorithm is analyzed, where both unimodal and multimodal optimization test functions are selected and optimized by GA, PSO, and ALO, respectively. The results indicate that the ALO algorithm has good global optimization capability and applicability. Second, the ALO algorithm is merged into the UKF to adjust the statistical properties of noise information for the ALOUKF estimator design without extra sensor signals. At last, the simulations on the Matlab/Simulink-CarSim co-simulation platform and the road test based on an A&D 5435 rapid prototyping experiment platform (RPP) are carried out to verify the proposed method. The simulation and experiment results demonstrate that the ALOUKF estimator can improve state estimation accuracy and resist the vehicle nonlinearity even in the case of the complicated and emergency maneuvers.

scholarly journals Vehicle Running State Estimation by Adaptive Soft-Sensing Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Liang Hao ◽  
Lixin Guo ◽  
Shuwei Liu
Keyword(s):  
Working Condition ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Lateral Acceleration ◽  
Measurement Information ◽  
Tire Model ◽  
Steering Angle ◽  
Soft Sensing ◽  
Adaptive Unscented Kalman Filter ◽  
Cost Measurement

Vehicle running state adaptive unscented Kalman filter soft-sensing algorithm is put forward in this paper based on traditional UKF which can estimate vehicle running state parameters and suboptimal Sage-Husa noise estimator which can effectively solve the problem of noises varying with time. Meanwhile 3-DOF dynamic model of vehicle and HSRI tire model are established. So vehicle running state can be accurately estimated by fusing the low-cost measurement information of longitudinal and lateral acceleration and handwheel steering angle. Under the typical working condition, AUKF soft-sensing algorithm is verified with substantial vehicle tests. Comparing with UKF soft-sensing algorithm, the result indicates AUKF soft-sensing algorithm has a good performance in robustness and is able to realize the effective estimation of vehicle running state more precisely than UKF soft-sensing algorithm.

scholarly journals Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation

Sensors ◽  
2016 ◽  
Vol 16 (9) ◽  
pp. 1530 ◽  
Author(s):  
Xi Liu ◽  
Hua Qu ◽  
Jihong Zhao ◽  
Pengcheng Yue ◽  
Meng Wang
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Relative State
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