scholarly journals A new application of the Extended Kalman Filter to the estimation of roll angles of a motorcycle with Inertial Measurement Unit

2020 ◽  
Vol 48 (2) ◽  
pp. 255-265
Author(s):  
Lorenzo Romualdi ◽  
Nicolò Mancinelli ◽  
Felice de ◽  
Silvio Sorrentino
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Inertial Measurement

scholarly journals Angular velocity fusion of the microelectromechanical system inertial measurement unit array based on extended Kalman filter with correlated system noises

2020 ◽  
pp. 002029402091770
Author(s):  
Li Xing ◽  
Xiaowei Tu ◽  
Weixing Qian ◽  
Yang Jin ◽  
Pei Qi
Keyword(s):  
Kalman Filter ◽  
Angular Velocity ◽  
Extended Kalman Filter ◽  
Inertial Measurement Unit ◽  
Angular Rate ◽  
The State ◽  
Measurement Unit ◽  
Fusion Method ◽  
Microelectromechanical System ◽  
Inertial Measurement

The paper proposes an angular velocity fusion method of the microelectromechanical system inertial measurement unit array based on the extended Kalman filter with correlated system noises. In the proposed method, an adaptive model of the angular velocity is built according to the motion characteristics of the vehicles and it is regarded as the state equation to estimate the angular velocity. The signal model of gyroscopes and accelerometers in the microelectromechanical system inertial measurement unit array is used as the measurement equation to fuse and estimate the angular velocity. Due to the correlation of the state and measurement noises in the presented fusion model, the traditional extended Kalman filter equations are optimized, so as to accurately and reliably estimate the angular velocity. By simulating angular rates in different motion modes, such as constant and change-in-time angular rates, it is verified that the proposed method can reliably estimate angular rates, even when the angular rate has been out of the microelectromechanical system gyroscope measurement range. And results show that, compared with the traditional angular rate fusion method of microelectromechanical system inertial measurement unit array, it can estimate angular rates more accurately. Moreover, in the kinematic vehicle experiments, the performance advantage of the proposed method is also verified and the angular rate estimation accuracy can be increased by about 1.5 times compared to the traditional method.

scholarly journals Globally stable velocity estimation using normalized velocity measurement

2019 ◽  
Vol 39 (1) ◽  
pp. 143-157
Author(s):  
Elias Bjørne ◽  
Edmund F Brekke ◽  
Torleiv H Bryne ◽  
Jeff Delaune ◽  
Tor Arne Johansen
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Inertial Measurement Unit ◽  
Velocity Estimation ◽  
Nonlinear Observer ◽  
Measurement Unit ◽  
Velocity Measurements ◽  
Nonlinear Methods ◽  
Inertial Measurement ◽  
Globally Stable

The problem of estimating velocity from a monocular camera and calibrated inertial measurement unit (IMU) measurements is revisited. For the presented setup, it is assumed that normalized velocity measurements are available from the camera. By applying results from nonlinear observer theory, we present velocity estimators with proven global stability under defined conditions, and without the need to observe features from several camera frames. Several nonlinear methods are compared with each other, also against an extended Kalman filter (EKF), where the robustness of the nonlinear methods compared with the EKF are demonstrated in simulations and experiments.

Velocity Estimation for Quadrupeds Based on Extended Kalman Filter

2014 ◽  
Vol 621 ◽  
pp. 525-532 ◽  
Author(s):  
Man Tian Li ◽  
Cong Wei Wang ◽  
Peng Fei Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Random Error ◽  
Inertial Measurement Unit ◽  
Quadruped Robot ◽  
Velocity Estimation ◽  
Measurement Unit ◽  
Forward Kinematics ◽  
Random Errors ◽  
Inertial Measurement

Measuring robots’ real-time velocity correctly is important for locomotion control. Inertial Measurement Unit (IMU) is widely used for velocity measurement. Limited by the bias and random error, IMU alone often can’t meet the requirement. This paper makes use of Extended Kalman Filter (EKF) to fuse kinematics and IMU, and inhibits the drift successfully. We calibrate the bias and recognize the random errors of IMU. Then the forward kinematics of legs is established and the EKF algorithm for velocity estimation is designed based on IMU and kinematics. Finally, the presented algorithm is validated in simulation and on a quadruped robot based on hydraulic driver in trotting gait.

Attitude Determination by Integration of Multi-Sensors Based on Federated Kalman Filter

2014 ◽  
Vol 602-605 ◽  
pp. 2958-2961
Author(s):  
Tao Lai ◽  
Guang Long Wang ◽  
Wen Jie Zhu ◽  
Feng Qi Gao
Keyword(s):  
Kalman Filter ◽  
Inertial Measurement Unit ◽  
Attitude Determination ◽  
Angular Rate ◽  
General Structure ◽  
Test System ◽  
Measurement Unit ◽  
Storage Test ◽  
Inertial Measurement ◽  
Federated Kalman Filter

Micro inertial measurement unit integration storage test system is a typical multi-sensor information fusion system consists of microsensors. The Federated Kalman filter is applied to micro inertial measurement unit integration storage test system. The general structure and characteristics of Federated Kalman filter is expounded. The four-order Runge-Kutta method based on quaternion differential equation was used to dispose the output angular rate data from gyroscope, and the recurrence expressions was established too. The control system based ARM Cortex-M4 master-slave structure is adopted in this paper. The result shown that the dimensionality reduced algorithm significantly reduces implementation complexity of the method and the amount computation. The filtering effect and real-time performance have much increased than traditionally method.

scholarly journals A Robust and Adaptive Complementary Kalman Filter Based on Mahalanobis Distance for Ultra Wideband/Inertial Measurement Unit Fusion Positioning

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3435 ◽  
Author(s):  
Xin Li ◽  
Yan Wang ◽  
Kourosh Khoshelham
Keyword(s):  
Kalman Filter ◽  
Mahalanobis Distance ◽  
Inertial Measurement Unit ◽  
Least Squares Method ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Observation Error ◽  
System State ◽  
Inertial Measurement ◽  
The Impact

Ultra wideband (UWB) has been a popular technology for indoor positioning due to its high accuracy. However, in many indoor application scenarios UWB measurements are influenced by outliers under non-line of sight (NLOS) conditions. To detect and eliminate outlying UWB observations, we propose a UWB/Inertial Measurement Unit (UWB/IMU) fusion filter based on a Complementary Kalman Filter to track the errors of position, velocity and direction. By using the least squares method, the positioning residual of the UWB observation is calculated, the robustness factor of the observation is determined, and an observation weight is dynamically set. When the robustness factor does not exceed a pre-defined threshold, the observed value is considered trusted, and adaptive filtering is used to track the system state, while the abnormity of system state, which might be caused by IMU data exceptions or unreasonable noise settings, is detected by using Mahalanobis distance from the observation to the prior distribution. When the robustness factor exceeds the threshold, the observed value is considered abnormal, and robust filtering is used, whereby the impact of UWB data exceptions on the positioning results is reduced by exploiting Mahalanobis distance. Experimental results show that the observation error can be effectively estimated, and the proposed algorithm can achieve an improved positioning accuracy when affected by outlying system states of different quantity as well as outlying observations of different proportion.

scholarly journals Aircraft Inertial Measurement Unit Fault Diagnosis Based on Optimal Two-Stage UKF

2018 ◽  
Vol 36 (5) ◽  
pp. 933-941
Author(s):  
Qizhi He ◽  
Weiguo Zhang ◽  
Degang Huang ◽  
Huakun Chen ◽  
Jinglong Liu
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Nonlinear System ◽  
Inertial Measurement Unit ◽  
Unscented Kalman Filter ◽  
Optimal Estimation ◽  
Measurement Unit ◽  
Two Stage ◽  
Inertial Measurement ◽  
System States

Optimal two stage Kalman filter (OTSKF) is able to obtain optimal estimation of system states and bias for linear system which contains random bias. Unscented Kalman filter (UKF) is a conventional nonlinear filtering method which utilizes Sigmas point sampling and unscented transformation technology realizes propagation of state means and covariances through nonlinear system. Aircraft is a typical complicate nonlinear system, this paper treats the faults of Inertial Measurement Unit (IMU) as random bias, established a filtering model which contains faults of IMU. Hybird the two stage filtering technique and UKF, this paper proposed an optimal two stage unscented Kalman filter (OTSUKF) algorithm which is suitable for fault diagnosis of IMU, realized optimal estimation of system states and faults identification of IMU via proposed innovative designing method of filtering model and the algorithm was validated that it is robust to wind disterbance via real flight data and it is also validated that proposed OTSUKF is optimal in the existance of wind disturbance via comparing with the existance iterated optimal two stage extended kalman filter (IOTSEKF) method.

scholarly journals Aircraft Inertial Measurement Unit Fault Diagnosis Based on Adaptive Two-Stage UKF

2020 ◽  
Vol 38 (4) ◽  
pp. 806-813
Author(s):  
Qizhi He ◽  
Weiguo Zhang ◽  
Xiaoxiong Liu ◽  
Weinan Li
Keyword(s):  
Kalman Filter ◽  
Random Walk ◽  
Fault Diagnosis ◽  
Inertial Measurement Unit ◽  
Unscented Kalman Filter ◽  
Random Walk Model ◽  
Measurement Unit ◽  
Actual Situation ◽  
Two Stage ◽  
Inertial Measurement

In the case of nonlinear systems with random bias, the Optimal Two-Stage Unscented Kalman Filter (OTSUKF) can obtain the optimal estimation of system state and bias. But it requires random bias to be accurately modeled, while it is always very difficult in actual situation because the aircraft is a typical nonlinear system. In this paper, the faults of the Inertial Measurement Unit (IMU) are treated as a random bias, and the random walk model is used to describe the fault. The accuracy of the random walk model depends on the degree of matching between the covariance of the random walk model and the actual situation. For the IMU fault diagnosis method based on OTSUKF, the covariance of the random walk model is assigned with a constant matrix, and the value of the matrix is initialized empirically. It is very difficult to select a matching matrix in practical applications. For this problem, in this paper, the covariance matrix of the random walk model is adaptively adjusted online based on the innovation covariance matching technique, and an adaptive Two-Stage Unscented Kalman Filter (ATSUKF) is proposed to solve the fault diagnosis problem of the IMU. The simulation experiment compares the IMU fault diagnosis performance of OTSUKF and ATSUKF, and verifies the effectiveness of the proposed adaptive method.

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