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.

Precise attitude determination of ship based on star sensor

2012 ◽  
Author(s):  
Bing Liu ◽  
Feng Chen ◽  
Tongshuang Zhang ◽  
Dean Zhong ◽  
Lei Yang ◽  
...  
Keyword(s):  
Attitude Determination ◽  
Star Sensor

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.

Precise Attitude Determination of Ship Based on Star Sensor

2013 ◽  
Vol 380-384 ◽  
pp. 995-1002 ◽  
Author(s):  
Bing Liu ◽  
Feng Chen ◽  
Tong Shuang Zhang ◽  
Dean Zhong ◽  
Lei Yang ◽  
...  
Keyword(s):  
Engineering Design ◽  
High Precision ◽  
Attitude Determination ◽  
Measured Data ◽  
Star Sensor ◽  
Single Star

This paper analyses the attitude measured model and presents the attitude determination algorithm of space TT&C ship (space tracking, telemetry, and command ship) based on single star sensor. Considering lower precision of rolling angel for single star sensor, we proposed an algorithm by integrating attitude determination and redundancy measure to obtain high precision ship attitude data. Aiming at the circumstance of space TT&C ship, the factors that influence the precision of attitude measured data such as the number of star, atmosphere refraction correct and installation elevation are analyzed, which this can provide valuable references to the engineering design for star sensor used on space TT&C ship.

Multiple-model adaptive estimator for spacecraft attitude sensor calibration

2017 ◽  
Vol 89 (3) ◽  
pp. 457-467 ◽  
Author(s):  
Kai Xiong ◽  
Chunling Wei
Keyword(s):  
Kalman Filter ◽  
Convergence Rate ◽  
Attitude Determination ◽  
Spacecraft Attitude ◽  
Star Sensor ◽  
Multiple Model ◽  
Content Type ◽  
Adaptive Estimator ◽  
Calibration Algorithm ◽  
Novel Method

Purpose This paper aims to present a multiple-model adaptive estimator (MMAE) to calibrate the star sensor low frequency error (LFE). The star sensor LFE, which is caused primarily by the periodic thermal distortion, has a great impact on spacecraft attitude determination accuracy. Design/methodology/approach The unfavorable effect of the LFE can be partly eliminated by using the calibration algorithm based on the augmented Kalman filter (AKF). However, the AKF may be worse than the traditional Kalman filter (KF) in the absence of the LFE. To cope with this problem, the MMAE is applied first time for combining the AKF and the KF in the spacecraft attitude determination system, such that satisfactory performance can be achieved in different operating scenarios. Findings The convergence of the presented MMAE is demonstrated through a formal derivation. A novel method is proposed to tune the MMAE design parameter, such that the convergence rate of the estimator is increased. It is shown via numerical studies that the presented algorithm outperforms the AKF and the KF. Practical implications The calibration algorithm is applicable for spacecraft attitude determination. Originality/value An effective star sensor LFE calibration algorithm based on the MMAE is developed. In addition, a novel method is proposed to increase convergence rate of the estimator.

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 Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

2009 ◽  
Vol 132 (3) ◽  
Author(s):  
Donald L. Simon ◽  
Sanjay Garg
Keyword(s):  
Kalman Filter ◽  
Estimation Error ◽  
Engine Performance ◽  
Aircraft Engine ◽  
Operating Conditions ◽  
Performance Estimation ◽  
Experimental Simulation ◽  
Tuning Parameter ◽  
Estimation Accuracy ◽  
On Line

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multivariable iterative search routine that seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared with the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy.

Assessment of 2D ultrasound fluid volume estimation accuracy in different shaped objects: an in vitro study

2019 ◽  
Vol 61 (2) ◽  
pp. 253-259
Author(s):  
Iroshani Kodikara ◽  
Iroshini Abeysekara ◽  
Dhanusha Gamage ◽  
Isurani Ilayperuma
Keyword(s):  
Estimation Error ◽  
In Vitro Study ◽  
High Accuracy ◽  
Volume Estimation ◽  
Estimation Accuracy ◽  
Rater Agreement ◽  
Actual Volume ◽  
2D Ultrasound ◽  
One Way Anova

Background Volume estimation of organs using two-dimensional (2D) ultrasonography is frequently warranted. Considering the influence of estimated volume on patient management, maintenance of its high accuracy is empirical. However, data are scarce regarding the accuracy of estimated volume of non-globular shaped objects of different volumes. Purpose To evaluate the volume estimation accuracy of different shaped and sized objects using high-end 2D ultrasound scanners. Material and Methods Globular (n=5); non-globular elongated (n=5), and non-globular near-spherical shaped (n=4) hollow plastic objects were scanned to estimate the volumes; actual volumes were compared with estimated volumes. T-test and one-way ANOVA were used to compare means; P<0.05 was considered significant. Results The actual volumes of the objects were in the range of 10–445 mL; estimated volumes ranged from 6.4–425 mL ( P=0.067). The estimated volume was lower than the actual volume; such volume underestimation was marked for non-globular elongated objects. Regardless of the scanner, the highest volume estimation error was for non-globular elongated objects (<40%) followed by non-globular near-spherical shaped objects (<23.88%); the lowest was for globular objects (<3.6%). Irrespective of the shape or the volume of the object, volume estimation difference among the scanners was not significant: globular (F=0.430, P=0.66); non-globular elongated (F=3.69, P=0.064); and non-globular near-spherical (F=4.00, P=0.06). A good inter-rater agreement (R=0.99, P<0.001) and a good correlation between actual versus estimated volumes (R=0.98, P<0.001) were noted. Conclusion The 2D ultrasonography can be recommended for volume estimation purposes of different shaped and different sized objects, regardless the type of the high-end scanner used.

Multi-spacecraft collaborative attitude determination of space tumbling target with experimental verification

2021 ◽  
Vol 185 ◽  
pp. 1-13
Author(s):  
Di Zhao ◽  
Chong Sun ◽  
Zhanxia Zhu ◽  
Wenya Wan ◽  
Zixuan Zheng ◽  
...  
Keyword(s):  
Experimental Verification ◽  
Attitude Determination ◽  
Tumbling Target
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