REKF and RUKF for pico satellite attitude estimation in the presence of measurement faults (original) (raw)
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ISA Transactions, 2010
In the normal operation conditions of a pico satellite, a conventional Unscented Kalman Filter (UKF) gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunction in the estimation system, UKF gives inaccurate results and diverges by time. This study introduces Robust Unscented Kalman Filter (RUKF) algorithms with the filter gain correction for the case of measurement malfunctions. By the use of defined variables named as measurement noise scale factor, the faulty measurements are taken into consideration with a small weight, and the estimations are corrected without affecting the characteristics of the accurate ones. Two different RUKF algorithms, one with single scale factor and one with multiple scale factors, are proposed and applied for the attitude estimation process of a pico satellite. The results of these algorithms are compared for different types of measurement faults in different estimation scenarios and recommendations about their applications are given.
European Journal of Control, 2014
In normal working conditions it is possible to achieve sufficient attitude estimation accuracy for a satellite using regular Kalman filter algorithm. On the other hand, when there is a fault in the measurements, the Kalman filter fails in providing the required accuracy and may even collapse over time. In this paper, a Robust Kalman filtering method is proposed for the attitude estimation problem. By using the proposed method both the Extended Kalman Filter and Unscented Kalman Filter are modified and the new algorithms, which are robust against measurement malfunctions, are called Robust Extended Kalman Filter and Robust Unscented Kalman Filter, respectively. A multiple scale factor based adaptation scheme is preferred for adapting the filters so only the data of the faulty sensor is scaled and any unnecessary information loss is prevented. The proposed algorithms are demonstrated for attitude estimation of a small satellite and performances of these two robust Kalman filters are compared in case of different measurement faults. The application of the algorithm is discussed for small satellite missions where the attitude accuracy depends on a limited number of measurements.
Robust adaptive unscented Kalman filter for attitude estimation of pico satellites
Unscented Kalman filter (UKF) is a filtering algorithm that gives sufficiently good estimation results for the estimation problems of nonlinear systems even when high nonlinearity is in question. However, in case of system uncertainty or measurement malfunctions, the UKF becomes inaccurate and diverges by time. This study introduces a fault-tolerant attitude estimation algorithm for pico satellites. The algorithm uses a robust adaptive UKF, which performs correction for the process noise covariance (Q-adaptation) or measurement noise covariance (R-adaptation) depending on the type of the fault. By the use of a newly proposed adaptation scheme for the conventional UKF algorithm, the fault is detected and isolated, and the essential adaptation procedure is followed in accordance with the fault type. The proposed algorithm is tested as a part of the attitude estimation algorithm of a pico satellite. it with a time-dependent variable. One of the methods for constructing such algorithm is to use a scale factor as a multiplier to the process or measurement noise covariance matrices . This kind of gain correction-based algorithms can be used when the information about the dynamic or measurement process is absent . Hence, the adaptation against both the system uncertainty and the measurement malfunctions is possible.
Adaptive Unscented Kalman Filter with multiple fading factors for pico satellite attitude estimation
2009 4th International Conference on Recent Advances in Space Technologies, 2009
Thus far, Kalman filter based attitude estimation algorithms have been used in many space applications. When the issue of pico satellite attitude estimation is taken into consideration, general linear approach to Kalman filter becomes insufficient and Extended Kalman Filters (EKF) are the types of filters, which are designed in order to overrun this problem. However, in case of attitude estimation of a pico satellite via magnetometer data, where the nonlinearity degree of both dynamics and measurement models are high, EKF may give inaccurate results. Unscented Kalman Filter (UKF) that does not require linearization phase and so Jacobians can be preferred instead of EKF in such circumstances. Nonetheless, if the UKF is built with an adaptive manner, such that, faulty measurements do not affect attitude estimation process, accurate estimation results even in case of measurement malfunctions can be guaranteed. In this study an Adaptive Unscented Kalman Filter with multiple fading factors based gain correction is introduced and tested on the attitude estimation system of a pico satellite by the use of simulations.
This paper presents an adaptive unscented Kalman filter (AUKF) to recover the satellite attitude in a fault detection and diagnosis (FDD) subsystem of microsatellites. The FDD subsystem includes a filter and an estimator with residual generators, hypothesis tests for fault detections and a reference logic table for fault isolations and fault recovery. The recovery process is based on the monitoring of mean and variance values of each attitude sensor behaviors from residual vectors. In the case of normal work, the residual vectors should be in the form of Gaussian white noise with zero mean and fixed variance. When the hypothesis tests for the residual vectors detect something unusual by comparing the mean and variance values with dynamic thresholds, the AUKF with realtime updated measurement noise covariance matrix will be used to recover the sensor faults. The scheme developed in this paper resolves the problem of the heavy and complex calculations during residual generations and therefore the delay in the isolation process is reduced. The numerical simulations for TSUBAME, a demonstration microsatellite of Tokyo Institute of Technology, are conducted and analyzed to demonstrate the working of the AUKF and FDD subsystem.
Sensor-fault tolerant attitude determination using two-stage estimator
Advances in Space Research, 2019
Satellite attitude determination accuracy significantly drops when sensorfault occurs. Hence, a proper mitigation strategy to detect sensor-fault and accurately estimate corresponding fault magnitudes is mandatory for robust and accurate attitude determination. In this paper, a novel sensor-fault tolerant precise attitude estimator is proposed consisting of two stages. In the first stage, sensor-fault is detected, and the associated sensor parameter change is roughly estimated using an interacting multiple-model (IMM) approach. Subsequently, the second stage is triggered. The sensor parameter change is precisely estimated with a new sensor-parameter-augmented filter. This is defined as a selectively augmented extended Kalman filter (SAEKF) in this paper. The conventional augmented extended Kalman filter (AEKF) is computationally more expensive than the proposed SAEKF. The SAEKF augments only the sensor parameters affected by sensor-faults, not the full sensor parameters, into the state vector. This leads to a significant computational time-saving. A transition method from the first stage to the second stage is also investigated. Numerical simulation results demonstrate that the proposed two-stage approach has smaller attitude determination errors than the existing algorithms, ranged from 21.7% to 88.8%, in cases with gyro scale factor error or misalignment.
Journal of Aerospace Technology and Management
The non-linear estimators are certainly the most important algorithms applied to real problems, especially those involving the attitude estimation of spacecraft. The purpose of this paper was to use real data of sensors to analyze the behavior of Unscented Kalman Filter (UKF) in attitude estimation problems when it is represented in different ways and compare it with the standard estimator for non-linear estimation problems. The robustness of the estimation was performed when this was subjected to imprecise initial conditions. The attitude parametrization was described in Euler angles, quaternion and quaternion incremental. The satellite China-Brazil Earth Resources Satellite and measurements provided by the Satellite Control Center of the Instituto Nacional de Pesquisas Espaciais were considered in the study. The results indicate that the behaviors for both estimators were equivalent for such parameterizations under the same conditions. However, comparing the Unscented Kalman Filter with the standard filter for non-linear systems, Extended Kalman Filter (EKF), it was observed that, in the presence of inaccurate initial conditions, the Unscented Kalman Filter presented a fast convergence whereas Extended Kalman Filter had problems and only converged later on.
2021
The satellite communication is embellished constantly by providing information, ensuring security, and enables the communication among huge at a particular time efficiently. The satellite navigation helps in determining the people's location. The global development, natural disasters, change in climatic conditions, agriculture crop growth, etc. are monitored using satellite observation. Hence, the satellite includes detailed information data, it has to be protected confidentially. The field of the satellite is enhanced at an astonishing pace. Satellite data plays an important role in this modern world and hence, the onboard satellite data have to secure through the proper selection of error detection and estimation schema. The Extended Kalman Filter is widely used in the satellite system. EKF is utilized in this proposed model to detect the onboard pointing error such as attitude and orbit determination. An autonomous estimation of orbit position is possible through space-borne ...
Comparison of attitude estimation methods for pico-satellites in low earth orbit
This paper compares attitude estimation methods that use microelectromechanical systems-attitude heading reference system (MEMS-AHRS) for underwater vehicle (UV) navigation. Although MEMS-AHRS is a cheap, lightweight, small, and easy-to-use instrument for attitude determination, the yaw estimate using the AHRS is not as reliable as the estimates of roll and pitch. This is because yaw estimation depends primarily on the magnetic field measurement, and the magnetic field measurement of the AHRS is vulnerable to magnetic interference induced by the vehicle and instrument itself and the environment surrounding the vehicle. This paper compares four major approaches: nonlinear explicit complementary filter (NECF), extended Kalman filter (EKF), sine rotation vector (SRV) method, and complementary filter (CF). The methods are tested through experiments in a test tank. The results show that the errors in yaw show notable differences between the methods. NECF and SRV show an improvement over the EKF and CF. This paper provides a practical comparison of the underwater attitude estimation methods through experiments, and the results can be used as a reference to be compared with other methods to be developed. In addition, this can help adapt the methods appropriate for a specific underwater application.
Unscented Kalman filter and smoothing applied to attitude estimation of artificial satellites
Computational and Applied Mathematics, 2018
This article uses the state smoothing methodology applied to nonlinear systems to refine the attitude of artificial satellites. In this paper, simulated data of telemetry and ephemeris of a satellite with the specifications of China Brazil Earth Resources Satellite are considered and the dynamic system is described by the set of kinematic equations in terms of the Euler angles and the bias vector of gyroscope. The estimator used to determine the forward estimates in time is the Unscented Kalman filter, while the Rauch-Tung-Striebel fixed interval estimator makes the estimate backward time. The results show that, although the time of the estimation process is slightly increased, the smoother presents estimated attitude and bias closer to the real values than the estimated values when using only the Unscented Kalman filter. Therefore, the smoother can be considered as a technique that provides refined measurements of the attitude and bias of the gyroscope that may serve to calibrate the Kalman filter for next estimates.