Residual Based Adaptive Unscented Kalman Filter for Satellite Attitude Estimation

2012 ◽  
Author(s):  
H. Ersin Soken ◽  
Shin-ichiro Sakai
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Attitude Estimation ◽  
Satellite Attitude ◽  
Adaptive Unscented Kalman Filter

scholarly journals Quaternion-Based Robust Attitude Estimation Using an Adaptive Unscented Kalman Filter

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2372 ◽  
Author(s):  
Antônio C. B. Chiella ◽  
Bruno O. S. Teixeira ◽  
Guilherme A. S. Pereira
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Ground Truth ◽  
Adaptive Strategy ◽  
Attitude Estimation ◽  
Covariance Matching ◽  
Adaptive Unscented Kalman Filter ◽  
Unit Quaternions ◽  
Abrupt Changes ◽  
Robust Adaptive

This paper presents the Quaternion-based Robust Adaptive Unscented Kalman Filter (QRAUKF) for attitude estimation. The proposed methodology modifies and extends the standard UKF equations to consistently accommodate the non-Euclidean algebra of unit quaternions and to add robustness to fast and slow variations in the measurement uncertainty. To deal with slow time-varying perturbations in the sensors, an adaptive strategy based on covariance matching that tunes the measurement covariance matrix online is used. Additionally, an outlier detector algorithm is adopted to identify abrupt changes in the UKF innovation, thus rejecting fast perturbations. Adaptation and outlier detection make the proposed algorithm robust to fast and slow perturbations such as external magnetic field interference and linear accelerations. Comparative experimental results that use an industrial manipulator robot as ground truth suggest that our method overcomes a trusted commercial solution and other widely used open source algorithms found in the literature.

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.

Multiplicative-modified-Rodrigues-parameters-based strong tracking unscented Kalman filter for satellite attitude estimation

2021 ◽  
Author(s):  
Haining Ma ◽  
Zhengliang Lu ◽  
Xiang Zhang ◽  
Wenhe Liao
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Simple Procedure ◽  
Weighted Average ◽  
Attitude Estimation ◽  
State Variables ◽  
Multiplicative Property ◽  
Satellite Attitude ◽  
Modified Rodrigues Parameters ◽  
Novel Method

Abstract In this paper, an improved strong tracking unscented Kalman filter (STUKF) based on multiplicative modified Rodrigues parameters (MRPs) is proposed for satellite attitude estimation. The multiplicative MRPs are superior to additive ones in terms of attitude representation, especially when attitude angles are large. By minimizing the loss function in Wahba’s problem, a novel method of weighted average for MRPs is derived to replace the simple procedure. The generation of Sigma points, update of state variables and calculation of covariance matrices are all different from the existing literature to maintain the multiplicative property of MRPs. Simulation results by raw telemetry data from the on-orbit CubeSat Enlai-1 demonstrate the excellent performance of the proposed filter under large attitude angles.

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