Combined Estimation of Vehicle Slip Angle and Lateral Tire Forces with an Unscented Kalman Filter with Outlier Detection

2019 ◽  
Author(s):  
Peter Speth ◽  
Michael Buchholz
Keyword(s):  
Kalman Filter ◽  
Outlier Detection ◽  
Unscented Kalman Filter ◽  
Slip Angle ◽  
Combined Estimation ◽  
Tire Forces

scholarly journals Erratum to: Identification of tire forces using Dual Unscented Kalman Filter algorithm

2014 ◽  
Vol 78 (4) ◽  
pp. 2985-2985 ◽  
Author(s):  
Iraj Davoodabadi ◽  
Asghar Ramezani ◽  
Mehdi Mahmoodi-k ◽  
Pouyan Ahmadizadeh
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Kalman Filter Algorithm ◽  
Tire Forces

State Estimation of a Robotic Vehicle With Six In-Wheel Drives Using Kalman Filter

2020 ◽  
Author(s):  
Hussein F. M. Ali ◽  
Se-Woong Oh ◽  
Youngshik Kim
Keyword(s):  
Kalman Filter ◽  
Dynamic Model ◽  
Angular Acceleration ◽  
Estimation Algorithm ◽  
Slip Angle ◽  
Test Environment ◽  
Slip Ratio ◽  
Tire Force ◽  
Robotic Vehicle ◽  
Tire Forces

Abstract This paper describes an estimation algorithm for a robotic vehicle with articulated suspension (RVAS) to estimate the vehicle velocity and acceleration states, and the tire forces. The RVAS is an unmanned ground vehicle based on a skid steering using an independent in-wheel motor at each wheel. The estimation algorithm consists of five parts. In the first part, a wheel state estimator estimates the wheel rotational speed and its angular acceleration using Kalman filter, which is used to estimate the longitudinal tire force distribution in the second part. The third part is to estimate respective longitudinal, lateral, and vertical speeds of the vehicle and wheels. Based on these speeds, the slip ratio and slip angle are estimated in the fourth part. In the fifth part, the vertical tire force is then estimated. For a simulation test environment, the RVAS dynamic model is developed using Matlab and Simulink. The RVAS model consists of five main parts which include in-wheel motor model, wheel dynamic model, Fiala tire model, arm dynamic model, and the sprung mass dynamic model. The estimation algorithm is then validated using the vehicle test data and different test scenarios. It is found from simulation results that the proposed estimation algorithm can estimate the vehicle states, longitudinal tire forces, and vertical tire forces efficiently.

scholarly journals Vehicle State Information Estimation with the Unscented Kalman Filter

2014 ◽  
Vol 6 ◽  
pp. 589397 ◽  
Author(s):  
Hongbin Ren ◽  
Sizhong Chen ◽  
Gang Liu ◽  
Kaifeng Zheng
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Piecewise Linear ◽  
Vehicle Speed ◽  
State Information ◽  
Active Safety Systems ◽  
Tire Forces ◽  
Safety Systems ◽  
Vehicle Active Safety

The vehicle state information plays an important role in the vehicle active safety systems; this paper proposed a new concept to estimate the instantaneous vehicle speed, yaw rate, tire forces, and tire kinemics information in real time. The estimator is based on the 3DoF vehicle model combined with the piecewise linear tire model. The estimator is realized using the unscented Kalman filter (UKF), since it is based on the unscented transfer technique and considers high order terms during the measurement and update stage. The numerical simulations are carried out to further investigate the performance of the estimator under high friction and low friction road conditions in the MATLAB/Simulink combined with the Carsim environment. The simulation results are compared with the numerical results from Carsim software, which indicate that UKF can estimate the vehicle state information accurately and in real time; the proposed estimation will provide the necessary and reliable state information to the vehicle controller in the future.

scholarly journals Robust State Estimation With Redundant Proprioceptive Sensors

2013 ◽  
Author(s):  
David Rollinson ◽  
Howie Choset ◽  
Stephen Tully
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Outlier Detection ◽  
Robust Estimation ◽  
Detection Method ◽  
Unscented Kalman Filter ◽  
Accurate Estimation ◽  
Articulated Robot ◽  
Proprioceptive Sensors

We present a framework for robust estimation of the configuration of an articulated robot using a large number of redundant proprioceptive sensors (encoders, gyros, accelerometers) distributed throughout the robot. Our method uses an Unscented Kalman Filter (UKF) to fuse the robot’s sensor measurements. The filter estimates the angle of each joint of the robot, enabling the accurate estimation of the robot’s kinematics even if not all modules report sensor readings. Additionally, a novel outlier detection method allows the the filter to be robust to corrupted accelerometer and gyro data.

Identification of tire forces using Dual Unscented Kalman Filter algorithm

2014 ◽  
Vol 78 (3) ◽  
pp. 1907-1919 ◽  
Author(s):  
Iraj Davoodabadi ◽  
Ali Asghar Ramezani ◽  
Mehdi Mahmoodi-k ◽  
Pouyan Ahmadizadeh
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Kalman Filter Algorithm ◽  
Tire Forces

scholarly journals An Enhanced Smartphone Indoor Positioning Scheme with Outlier Removal Using Machine Learning

2021 ◽  
Vol 13 (6) ◽  
pp. 1106
Author(s):  
Zhenbing Zhang ◽  
Jingbin Liu ◽  
Lei Wang ◽  
Guangyi Guo ◽  
Xingyu Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Outlier Detection ◽  
Unscented Kalman Filter ◽  
Dead Reckoning ◽  
Maximum Error ◽  
Indoor Positioning ◽  
Fusion Scheme ◽  
Hybrid Fusion ◽  
Pedestrian Dead Reckoning

In smartphone indoor positioning, owing to the strong complementarity between pedestrian dead reckoning (PDR) and WiFi, a hybrid fusion scheme of them is drawing more and more attention. However, the outlier of WiFi will easily degrade the performance of the scheme, to remove them, many researches have been proposed such as: improving the WiFi individually or enhancing the scheme. Nevertheless, due to the inherent received signal strength (RSS) variation, there still exist some unremoved outliers. To solve this problem, this paper proposes the first outlier detection and removal strategy with the aid of Machine Learning (ML), so called WiFi-AGNES (Agglomerative Nesting), based on the extracted positioning characteristics of WiFi when the pedestrian is static. Then, the paper proposes the second outlier detection and removal strategy, so called WiFi-Chain, based on the extracted positioning characteristics of WiFi, PDR, and their complementary characteristics when the pedestrian is walking. Finally, a hybrid fusion scheme is proposed, which integrates the two proposed strategies, WiFi, PDR with an inertial-navigation-system-based (INS-based) attitude heading reference system (AHRS) via Extended Kalman Filter (EKF), and an Unscented Kalman Filter (UKF). The experiment results show that the two proposed strategies are effective and robust. With WiFi-AGNES, the minimum percentage of the maximum error (MaxE) is reduced by 66.5%; with WiFi-Chain, the MaxE of WiFi is less than 4.3 m; further the proposed scheme achieves the best performance, where the root mean square error (RMSE) is 1.43 m. Moreover, since characteristics are universal, the proposed scheme integrated the two characteristic-based strategies also possesses strong robustness.

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