Comparisons between Two Kinds of Star Sensor Measurement Models by Attitude Estimation Accuracy

2011 ◽  
pp. 291-294
Author(s):  
Zhigang Wang ◽  
Yifan Deng
Keyword(s):  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Star Sensor ◽  
Measurement Models ◽  
Sensor Measurement

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.

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 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 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 Effect of Strapdown Integration Order and Sampling Rate on IMU-Based Attitude Estimation Accuracy

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2775 ◽  
Author(s):  
Jung Lee ◽  
Mi Choi
Keyword(s):  
Low Cost ◽  
Sampling Rate ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Third Order ◽  
Process Noise ◽  
Noise Covariance Matrix ◽  
First Order ◽  
Noise Covariance ◽  
Integration Order

This paper deals with the strapdown integration of attitude estimation Kalman filter (KF) based on inertial measurement unit (IMU) signals. In many low-cost wearable IMU applications, a first-order is selected for strapdown integration, which may degrade attitude estimation performance in high-speed angular motions. The purpose of this research is to provide insights into the effect of the strapdown integration order and sampling rate on the attitude estimation accuracy for low-cost IMU applications. Experimental results showed that the effect of integration order was small when the angular velocity was low and the sampling rate was large. However, as the angular velocity increased and the sampling rate decreased, the effect of integration order increased, i.e., obviously, the third-order KF resulted in better estimations than the first-order KF. When comparing the case where both transient matrix and process noise covariance matrix are applied to the corresponding order and the case where only the transient matrix is applied to the corresponding order but the process noise covariance matrix for the first-order is still used, both cases had almost equivalent estimation accuracy. However, in terms of the calculation cost, the latter case was more economical than the former, particularly for the third-order KF (i.e., the ratio of the former to the latter is 1.22 to 1).

scholarly journals Pipeline Gaussian Particle Filter and Hardware Design for Attitude Estimation with UAV

2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Yali Xue ◽  
Hu Chen ◽  
Jie Chen ◽  
Jiahui Wang
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Gaussian Noise ◽  
Estimation Algorithm ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Gaussian State ◽  
Time Performance ◽  
Simulation Results ◽  
Non Gaussian

This paper based on the Gaussian particle filter (GPF) deals with the attitude estimation of UAV. GPF algorithm has better estimation accuracy than the general nonlinear non-Gaussian state estimation and is usually used to improve the system’s real-time performance whose noise is specific such as Gaussian noise during the mini UAV positioning and navigation. The attitude estimation algorithm is implemented on FPGA to verify the effectiveness of the Gaussian particle filter. Simulation results have illustrated that the GPF algorithm is effective and has better real-time performance than that of the particle filter.

High Accuracy Attitude Estimation for Spacecraft Using Quaternion Star Sensors and Gyros

2013 ◽  
Vol 427-429 ◽  
pp. 1544-1547
Author(s):  
Zhi Gang Wang ◽  
Yi Fan Deng
Keyword(s):  
Numerical Simulations ◽  
Optical Axis ◽  
High Accuracy ◽  
Attitude Estimation ◽  
The Other ◽  
Estimation Accuracy ◽  
Spacecraft Attitude ◽  
Novel Method

The problem of spacecraft attitude estimation utilizing star sensors and gyros is considered in this paper. Since the errors of the quaternion outputs of star sensors projected on the direction along the optical axis is higher than the other two directions, a novel method to increase the estimation accuracy of the attitude filter is presented, by producing observation vectors along the direction of the light axes, which are composed of a couple of quaternion outputs from different star sensors. The proposed algorithm is validated and proved more precise by numerical simulations.

scholarly journals Examining differences in streamflow estimation for gauged and ungauged catchments using evolutionary data assimilation

2013 ◽  
Vol 16 (2) ◽  
pp. 392-406 ◽  
Author(s):  
Gift Dumedah ◽  
Paulin Coulibaly
Keyword(s):  
Data Assimilation ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Model Parameter ◽  
Soil Moisture Accounting ◽  
Measurement Models ◽  
Daily Streamflow ◽  
Parameter Values ◽  
Ungauged Watersheds

Data assimilation has allowed hydrologists to account for imperfections in observations and uncertainties in model estimates. Typically, updated members are determined as a compromised merger between observations and model predictions. The merging procedure is conducted in decision space before model parameters are updated to reflect the assimilation. However, given the dynamics between states and model parameters, there is limited guarantee that when updated parameters are applied into measurement models, the resulting estimate will be the same as the updated estimate. To account for these challenges, this study uses evolutionary data assimilation (EDA) to estimate streamflow in gauged and ungauged watersheds. EDA assimilates daily streamflow into a Sacramento soil moisture accounting model to determine updated members for eight watersheds in southern Ontario, Canada. The updated members are combined to estimate streamflow in ungauged watersheds where the results show high estimation accuracy for gauged and ungauged watersheds. An evaluation of the commonalities in model parameter values across and between gauged and ungauged watersheds underscore the critical contributions of consistent model parameter values. The findings show a high degree of commonality in model parameter values such that members of a given gauged/ungauged watershed can be estimated using members from another watershed.

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