scholarly journals Linear Kalman Filter for Attitude Estimation from Angular Rate and a Single Vector Measurement

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Shangqiu Shan ◽  
Zhongxi Hou ◽  
Jin Wu
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Angular Rate ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Kinematic Equation ◽  
Vector Measurement ◽  
Vector Observation ◽  
Execution Speed ◽  
External Acceleration

In this paper, a new Kalman filtering scheme is designed in order to give the optimal attitude estimation with gyroscopic data and a single vector observation. The quaternion kinematic equation is adopted as the state model while the quaternion of the attitude determination from a strapdown sensor is treated as the measurement. Derivations of the attitude solution from a single vector observation along with its variance analysis are presented. The proposed filter is named as the Single Vector Observation Linear Kalman filter (SVO-LKF). Flexible design of the filter facilitates fast execution speed with respect to other filters with linearization. Simulations and experiments are conducted in the presence of large external acceleration and magnetic distortion. The results show that, compared with representative filtering methods and attitude observers, the SVO-LKF owns the best estimation accuracy and it consumes much less time in the fusion process.

scholarly journals Novel MARG-Sensor Orientation Estimation Algorithm Using Fast Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Siwen Guo ◽  
Jin Wu ◽  
Zuocai Wang ◽  
Jide Qian
Keyword(s):  
Kalman Filter ◽  
Process Model ◽  
Attitude Determination ◽  
Angular Rate ◽  
Estimation Algorithm ◽  
Measurement Model ◽  
Estimation Accuracy ◽  
Orientation Estimation ◽  
Kinematic Equation ◽  
Sensor Orientation

Orientation estimation from magnetic, angular rate, and gravity (MARG) sensor array is a key problem in mechatronic-related applications. This paper proposes a new method in which a quaternion-based Kalman filter scheme is designed. The quaternion kinematic equation is employed as the process model. With our previous contributions, we establish the measurement model of attitude quaternion from accelerometer and magnetometer, which is later proved to be the fastest (computationally) one among representative attitude determination algorithms of such sensor combination. Variance analysis is later given enabling the optimal updating of the proposed filter. The algorithm is implemented on real-world hardware where experiments are carried out to reveal the advantages of the proposed method with respect to conventional ones. The proposed approach is also validated on an unmanned aerial vehicle during a real flight. Results show that the proposed one is faster than any other Kalman-based ones and even faster than some complementary ones while the attitude estimation accuracy is maintained.

scholarly journals Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations

2016 ◽  
Vol 2016 ◽  
pp. 1-24 ◽  
Author(s):  
Romy Budhi Widodo ◽  
Chikamune Wada
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Compensation Model ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
The One ◽  
The Right ◽  
External Acceleration

Attitude estimation is often inaccurate during highly dynamic motion due to the external acceleration. This paper proposes extended Kalman filter-based attitude estimation using a new algorithm to overcome the external acceleration. This algorithm is based on an external acceleration compensation model to be used as a modifying parameter in adjusting the measurement noise covariance matrix of the extended Kalman filter. The experiment was conducted to verify the estimation accuracy, that is, one-axis and multiple axes sensor movement. Five approaches were used to test the estimation of the attitude: (1) the KF-based model without compensating for external acceleration, (2) the proposed KF-based model which employs the external acceleration compensation model, (3) the two-step KF using weighted-based switching approach, (4) the KF-based model which uses thethreshold-basedapproach, and (5) the KF-based model which uses the threshold-based approach combined with a softened part approach. The proposed algorithm showed high effectiveness during the one-axis test. When the testing conditions employed multiple axes, the estimation accuracy increased using the proposed approach and exhibited external acceleration rejection at the right timing. The proposed algorithm has fewer parameters that need to be set at the expense of the sharpness of signal edge transition.

scholarly journals Lightweight Extended Kalman Filter for MARG Sensors Attitude Estimation

2021 ◽  
Author(s):  
Lei Jing
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Process Model ◽  
Angular Rate ◽  
Measurement Model ◽  
Attitude Estimation ◽  
Human Motion ◽  
Computational Time ◽  
Orientation Estimation ◽  
Kinematic Equation

<div> <div> <div> <p>Low-power consumption of orientation estimation using low-cost inertial sensors are crucial for all the applications which are resource constrained critically. This paper presents a novel Lightweight quaternion-based Extended Kalman Filter (LEKF) for orientation estimation for magnetic, angular rate and gravity (MARG) sensors. In this filter, with employing the quaternion kinematic equation as the process model, we derived a simplified measurement model to create the lightweight system model for Kalman filtering, where the measurement model works efficiently and the involved computation of measurement model is reduced. It’s later proved that the proposed filter saves time consumption. Further, due to that no linearization is involved for the proposed measurement model, the good performance would be guaranteed in theory. For the experiments, a commercial sensor for data collection and an optical system to provide reference measurements of orientation, namely Vicon, are utilized to investigate the performance of the proposed filter. Evaluation for different application scenarios are considered, which primarily includes human motion capture and the drone application. Results indicate that the proposed filter provides reliable performance for both applications. What’s more, the comparison experiment shows that the proposed filter provides better performance in terms of either attitude estimation accu- racy or computational time. </p> </div> </div> </div>

scholarly journals Lightweight Extended Kalman Filter for MARG Sensors Attitude Estimation

2021 ◽  
Author(s):  
Lei Jing
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Process Model ◽  
Angular Rate ◽  
Measurement Model ◽  
Attitude Estimation ◽  
Human Motion ◽  
Computational Time ◽  
Orientation Estimation ◽  
Kinematic Equation

<div> <div> <div> <p>Low-power consumption of orientation estimation using low-cost inertial sensors are crucial for all the applications which are resource constrained critically. This paper presents a novel Lightweight quaternion-based Extended Kalman Filter (LEKF) for orientation estimation for magnetic, angular rate and gravity (MARG) sensors. In this filter, with employing the quaternion kinematic equation as the process model, we derived a simplified measurement model to create the lightweight system model for Kalman filtering, where the measurement model works efficiently and the involved computation of measurement model is reduced. It’s later proved that the proposed filter saves time consumption. Further, due to that no linearization is involved for the proposed measurement model, the good performance would be guaranteed in theory. For the experiments, a commercial sensor for data collection and an optical system to provide reference measurements of orientation, namely Vicon, are utilized to investigate the performance of the proposed filter. Evaluation for different application scenarios are considered, which primarily includes human motion capture and the drone application. Results indicate that the proposed filter provides reliable performance for both applications. What’s more, the comparison experiment shows that the proposed filter provides better performance in terms of either attitude estimation accu- racy or computational time. </p> </div> </div> </div>

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 Comparative Analysis of Low Earth Orbit Satellite Attitude Determination using Sensor Integration and Attitude Estimation using Kalman Filter

2021 ◽  
Vol 13 (2) ◽  
pp. 117-132
Author(s):  
M. RAJA
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Unscented Kalman Filter ◽  
Filter Design ◽  
Attitude Estimation ◽  
Matrix Form ◽  
Convergence Time ◽  
Linear Filters ◽  
Satellite Attitude ◽  
Non Linear

The objective of the research is to undergo a performance comparison in terms of accuracy, convergence time, amount of memory, etc. between the satellite attitude determination and attitude estimation using non-linear filters. The fundamental approach towards it is to design an OBC (On Board Computer) that would help in achieving a controlled output for the chosen plant (MicroMAS1 satellite). The attitude determination algorithm is implemented through TRIAD algorithm, which takes sensor readings of body frame and inertial frame of reference. Then it is used to determine the rotation matrix (DCM) by converting the matrix form into vector form and again back to matrix form to determine the 3x3 matrix, which includes all the Euler angle equations to determine the pitch, roll, yaw characteristics of the system. The attitude estimation algorithms involves the use of nonlinear filters which provide an added advantage that energy can be transferred in a designed manner and extra degrees of freedom are available in filter design. The Unscented Kalman Filter (UKF) is preferred as it addresses the problem using a deterministic sampling approach. Moreover, the non-linear filters are used to remove the noise error and disturbance caused by engine. The design of satellite attitude models involves more of a mathematical approach that would be dealt with MATLAB and SIMULINK operations.

scholarly journals Design of an Extended Interacting Multiple Models Adaptive Estimator for Attitude Determination of a Stereoimagery Satellite

2011 ◽  
Vol 2011 ◽  
pp. 1-19 ◽  
Author(s):  
Hossein Bolandi ◽  
Farhad Fani Saberi ◽  
Amir Mehrjardi Eslami
Keyword(s):  
Attitude Determination ◽  
Estimation Error ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Multiple Models ◽  
Uniform Motion ◽  
Star Sensor ◽  
Adaptive Estimator ◽  
Motion Mode

We will design an extended interacting multiple models adaptive estimator (EIMMAE) for attitude determination of a stereoimagery satellite. This algorithm is based on interacting multiple models (IMM) extended kalman filters (EKF) using star sensor and gyroscope data. In this method, the motion of satellite during stereoimaging manoeuvres is partitioned into two different modes: “manoeuvring motion” mode and “uniform motion” mode. The proposed algorithm will select the suitable Kalman filter structure to estimate gyro errors accurately in order to maintain the peak attitude estimation error less enough at the beginning of manoeuvres while the satellite is in “manoeuvring motion” mode and then will select the suitable star sensor measurement noise level at the end of manoeuvres while the satellite is in “uniform motion” mode to reduce attitude estimation errors. It will be shown that using the proposed algorithm, the attitude estimation accuracy of stereoimagery satellite will be increased considerably. The effectiveness of the proposed algorithm will be examined and compared with the previous proposed methods through numerical simulations.

Strong tracking central difference Kalman filter for CubeSat attitude estimation with status mutation

2021 ◽  
pp. 014233122110195
Author(s):  
Haining Ma ◽  
Zhengliang Lu ◽  
Xiang Zhang ◽  
Wenhe Liao
Keyword(s):  
Kalman Filter ◽  
Attitude Estimation ◽  
Second Order ◽  
Tracking Performance ◽  
Estimation Accuracy ◽  
Central Difference ◽  
Approximation Accuracy ◽  
Estimation System ◽  
Fading Factor ◽  
Central Difference Kalman Filter

This paper addressed a strongly nonlinear problem caused by status mutation in CubeSat attitude estimation system. The multiple fading factors are employed to make different state channels have separate adjustment ability, which enhances the tracking performance for status mutation. The second-order difference transformation is adopted to improve the approximation accuracy of state posterior mean and covariance. Therefore, a multiple fading second-order central difference Kalman filter (MFSCDKF) is formed for CubeSat attitude estimation system in the presence of status mutation. Compared with the single fading factor first-order central difference Kalman filter, the proposed one can improve tracking performance and estimation accuracy simultaneously. Simulation results based on real telemetry data from the on-orbit CubeSat NJUST-1 verify the effectiveness of the proposed MFSCDKF.

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