Unscented predictive variable structure filter for satellite attitude estimation with model errors when using low precision sensors

Cao, Lu; Li, Hengnian

doi:10.1016/j.actaastro.2016.06.045

Cited by 22 publications

(12 citation statements)

References 18 publications

(25 reference statements)

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“…The above variations can be utilized as a measure of fault detection. To minimize the measurement noise effect and to increase the accuracy of the designed mechanism, a variance measure is defined as equation (16) in which all Euler angles computation methods have been used…”

Section: Fault Detection Algorithmmentioning

confidence: 99%

“…This table has been assigned for fault occurrence in only one component of sensors (class 1). Now, for each of the six components in Table 2, the variance measures should be defined similar to equation (16). For example, about the y component of the magnetic sensor, we have…”

Section: Fault Detection Algorithmmentioning

confidence: 99%

“…A framework for the design of attitude estimations has been presented in Bahrami and Namvar 15 considering a single vector measurement with time delay. An unscented predictive variable structure filter has been inspected in Cao and Li 16 using low precision sensors. Also, a gyroless approach has been pursued in Hajiyev and Guler 17 using KFs.…”

Section: Introductionmentioning

confidence: 99%

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Design of a supervisory algorithm based on failure mode effect criticality analysis for classification of different fault types of attitude determination sensors

Abedi

2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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In this paper, a supervisory algorithm for attitude determination sensors of a three-axis satellite is suggested to classify different fault types of this system. The presented solution is based on the computation of rotation matrices between the orbital and body frames. Using the Euler angles provided by these rotations, some variance measures are computed, which utilized as an analytical tool for the determination of faulty sensors and the related fault sources. According to the mentioned idea, it is shown that the fault sources could be categorized into four levels. Each level shows the fault extent and its criticality. To include the reliability aspects in this paper, a failure mode effect criticality analysis is conducted to determine different failure modes, which really occur in sensors. For this, the probability levels and the severity numbers were assigned to derive the critical ones. It is shown that most of the critical failure modes could be isolated by the proposed methods; in other words, the design process is based on real faulty scenarios that can improve the system reliability. Finally, by performing some numerical simulations, performance of the developed algorithms are validated.

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Section: Fault Detection Algorithmmentioning

confidence: 99%

Section: Fault Detection Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design of a supervisory algorithm based on failure mode effect criticality analysis for classification of different fault types of attitude determination sensors

Abedi

2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…In order to solve the problem that the unknown manoeuvre is difficult to model, some novel filtering methods have been proposed in recent years. For example, Cao et al (2015; 2017) and Cao and Li (2016) proposed the predictive variable structure filter for estimating the relative position and attitude of satellites. In these studies, a predictive variable structure filter was used to estimate the dynamic model error.…”

Section: Introductionmentioning

confidence: 99%

Extended Kalman Filter with Input Detection and Estimation for Tracking Manoeuvring Satellites

et al. 2019

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The technique of tracking a non-cooperative manoeuvring satellite is important for Space Situation Awareness (SSA). However, the classical extended Kalman filter cannot work successfully in this situation. Motivated by this problem, a novel Extended Kalman Filter with Input Detection and Estimation (EKF/IDE) method is proposed in this paper for tracking a non-cooperative satellite with impulsive manoeuvres. The impulsive manoeuvre is modelled as an unknown acceleration without any prior information. An unbiased minimum-variance input and state estimation method is introduced to estimate the manoeuvre acceleration. An approach based on the Mahalanobis distance of the manoeuvre estimate error is proposed for manoeuvre detection. With the impulsive manoeuvre being detected and estimated accurately, an adaptive extended Kalman filter is proposed to estimate the state of the target. An approach of covariance inflation is proposed to deal with the manoeuvre during the unobserved period. Simulations and Monte Carlo experiments are implemented to demonstrate the feasibility and validity of the proposed method. The results of simulations show that the proposed method can accurately detect and estimate unknown impulsive manoeuvres of a non-cooperative satellite. Through the compensation of the estimated manoeuvre, the estimation of position and velocity after the manoeuvre maintains the accuracy of the pre-manoeuvre period, demonstrating the robustness of the proposed method against unknown manoeuvres.

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“…The first problem is that it is almost impossible to establish an accurate nonlinear mathematical model for attitude estimation. There are various perturbations from space that act on the cluster of satellites, which cannot be modelled accurately (Cao and Li, 2016; Cao et al, 2017), such as Earth non-spherical perturbation, atmospheric drag and other unknown model errors. In addition, the attitude dynamics model is sometimes employed instead of an inertial gyro to estimate the angular velocity, which will also inevitably introduce model errors.…”

Section: Introductionmentioning

confidence: 99%

Strong Tracking Sigma Point Predictive Variable Structure Filter for Attitude Synchronisation Estimation

et al. 2018

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In this paper, a novel Strong Tracking Sigma-Point Predictive Variable Structure Filter (ST-SP-PVSF) is presented as a further development of the Adaptive Predictive Variable Structure Filter (APVSF) for attitude synchronisation during Satellite Formation Flying (SFF). First, the sequence orthogonal principle is adopted to enhance the robustness of the APVSF for any nonlinear system with uncertain model errors. Then, sigma-point sampling strategies (such as unscented transfer, cubature rule and Stirling's polynomial interpolation) are introduced to extend the APVSF with the ability to capture the second central moment's information on the model errors to update the system model with higher precision. The new methodology has advantages in dealing with the various types of uncertainties or model errors compared with the APVSF. In addition, it does not need to choose the limit boundary layer ψlim it for system estimation, which reduces the sensitivity to the initial parameters and improves its adaptive ability over the APVSF. Simulations are performed to demonstrate that the proposed method is more suitable for attitude synchronisation estimation of the SFF system.

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Unscented predictive variable structure filter for satellite attitude estimation with model errors when using low precision sensors

Cited by 22 publications

References 18 publications

Design of a supervisory algorithm based on failure mode effect criticality analysis for classification of different fault types of attitude determination sensors

Design of a supervisory algorithm based on failure mode effect criticality analysis for classification of different fault types of attitude determination sensors

Extended Kalman Filter with Input Detection and Estimation for Tracking Manoeuvring Satellites

Strong Tracking Sigma Point Predictive Variable Structure Filter for Attitude Synchronisation Estimation

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