scholarly journals Finite-Horizon Robust Kalman Filter for Uncertain Attitude Estimation System with Star Sensor Measurement Delays

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Hua-Ming Qian ◽  
Wei Huang ◽  
Biao Liu
Keyword(s):  
Kalman Filter ◽  
Process Model ◽  
Estimation Error ◽  
Attitude Estimation ◽  
Finite Horizon ◽  
Star Sensor ◽  
Model Uncertainties ◽  
Robust Kalman Filter ◽  
Sensor Measurement ◽  
Estimation System

This paper addresses the robust Kalman filtering problem for uncertain attitude estimation system with star sensor measurement delays. Combined with the misalignment errors and scale factor errors of gyros in the process model and the misalignment errors of star sensors in the measurement model, the uncertain attitude estimation model can be established, which indicates that uncertainties not only appear in the state and output matrices but also affect the statistic of the process noise. Meanwhile, the phenomenon of star sensor measurement delays is described by introducing Bernoulli random variables with different delay characteristics. The aim of the addressed attitude estimation problem is to design a filter such that, in the presence of model uncertainties and star sensors delays for the attitude estimation system, the optimized filter parameters can be obtained to minimize the upper bound on the estimation error covariance. Therefore, a finite-horizon robust Kalman filter is proposed to cope with this question. Compared with traditional attitude estimation algorithms, the designed robust filter takes into account the effects of star sensor measurement delays and model uncertainties. Simulation results illustrate the effectiveness of the developed robust filter.

scholarly journals Finite-horizon robust Kalman filter design

2001 ◽  
Vol 49 (9) ◽  
pp. 2103-2112 ◽  
Author(s):  
Minyue Fu ◽  
C.E. de Souza ◽  
Zhi-Quan Luo
Keyword(s):  
Kalman Filter ◽  
Filter Design ◽  
Finite Horizon ◽  
Robust Kalman Filter

Multiple Robust High-degree Cubature Kalman Filter for Relative Position and Attitude Estimation of Satellite Formation

2019 ◽  
Vol 72 (5) ◽  
pp. 1254-1274 ◽  
Author(s):  
Ning Li ◽  
Wentao Ma ◽  
Weishi Man ◽  
Liu Cao ◽  
Hui Zhang
Keyword(s):  
Kalman Filter ◽  
State Space ◽  
High Dimension ◽  
Relative Position ◽  
Attitude Estimation ◽  
The State ◽  
Robust Performance ◽  
Cubature Kalman Filter ◽  
Estimation System ◽  
High Degree

The High-degree Cubature Kalman Filter (HCKF) is proposed as a novel methodology based on the arbitrary degree spherical rule, which can achieve better performance than the traditional Kalman filter. However, it also has a large calculation burden when used in a high-dimension and high-degree of accuracy estimation system. The number of sampling points of an HCKF increases polynomially with increasing state-space dimensions, which further increases the calculation burden. The reduction of the number of the state-space dimensions is the main contribution of this study. A strategy for HCKF based on the partitioning of the state-space and orthogonal principle is introduced, referred to as the Multiple Robust HCKF (MRHCKF). It is shown that this technique can effectively reduce the calculation burden for the high-dimension system with robust performance. Numerical simulations are performed for the example of high-dimension relative position and attitude estimation to show that the proposed method can obtain nearly the same performance as the HCKF, while drastically reducing computational complexity.

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 On the Relation Between Smooth Variable Structure and Adaptive Kalman Filter

2020 ◽  
Vol 6 ◽  
Author(s):  
Mark Spiller ◽  
Dirk Söffker
Keyword(s):  
Kalman Filter ◽  
Nonlinear Systems ◽  
State Estimation ◽  
Parameter Optimization ◽  
Estimation Error ◽  
Variable Structure ◽  
Uncertain Nonlinear Systems ◽  
Adaptive Kalman Filter ◽  
Model Uncertainties ◽  
Smooth Variable Structure Filter

This article is addressed to the topic of robust state estimation of uncertain nonlinear systems. In particular, the smooth variable structure filter (SVSF) and its relation to the Kalman filter is studied. An adaptive Kalman filter is obtained from the SVSF approach by replacing the gain of the original filter. Boundedness of the estimation error of the adaptive filter is proven. The SVSF approach and the adaptive Kalman filter achieve improved robustness against model uncertainties if filter parameters are suitably optimized. Therefore, a parameter optimization process is developed and the estimation performance is studied.

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.

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 Towards a Model Based Sensor Measurement Variance Input for Extended Kalman Filter State Estimation

Drones ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 19 ◽  
Author(s):  
Harry Pointon ◽  
Benjamin McLoughlin ◽  
Christian Matthews ◽  
Frederic Bezombes
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Optical Measurement ◽  
Measurement Instruments ◽  
Real World Data ◽  
Model Based ◽  
Sensor Measurement ◽  
Measurement Variance ◽  
Estimation System

In this paper, we present an alternate method for the generation and implementation of the sensor measurement variance used in an Extended Kalman Filter (EKF). Furthermore, it demonstrates the limitations of a conventional EKF implementation and postulates an alternate form for representing the sensor measurement variance by extending and improving the characterisation methodology presented in the previous work. As presented in earlier work, the use of surveying grade optical measurement instruments allows for a more effective characterisation of Ultra-Wide Band (UWB) localisation sensors; however, in cluttered environments, the sensor measurement variance will change, making this method not robust. To compensate for the noisier readings, an EKF using a model based sensor measurement variance was developed. This approach allows for a more accurate representation of the sensor measurement variance and leads to a more robust state estimation system. Simulations were run using synthetic data in order to test the effectiveness of the EKF against the originally developed EKF; next, the new EKF was compared to the original EKF using real world data. The new EKF was shown to function much more stably and consistently in less ideal environments for UWB deployment than the previous version.

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