Evaluation of Murrell’s EKF-Based Attitude Estimation Algorithm for Exploiting Multiple Attitude Sensor Configurations (original) (raw)
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Use of Extended Kalman Filter in Estimation of Attitude of a Nano-Satellite
International Journal of Electronics and Electrical Engineering, 2014
State estimation theory is one of the best mathematical approaches to analyze the changes in the states of a system or a process. The state of the system is defined by a set of variables that provide a complete representation of the internal conditions of the system at any given time instant. There are two types of state modelslinear state model and non-linear state model, which require different estimation techniques. Linear estimation of a system can be easily carried out by using Kalman Filter (KF), when the state space model is linear. But, most of the real life state models are nonlinear, thereby limiting the practical applications of the KF. The Extended Kalman Filter, Unscented Kalman filter and Particle filter are most commonly used for nonlinear estimation. EKF is the nonlinear version of the Kalman filters which revolves about the mean and covariance at the current time instant. The estimation can be linearized around the current estimate using the partial derivatives to compute estimates even in the nonlinear relations. This paper deals with estimation of various parameters of a nonlinear model with Extended Kalman filter (EKF).The paper analyses EKF method of estimating and then determining the attitude of the satellite depending upon the readings from the magnetometer.
Sensors, 2016
Most satellites use an on-board attitude estimation system, based on available sensors. In the case of low-cost satellites, which are of increasing interest, it is usual to use magnetometers and Sun sensors. A Kalman filter is commonly recommended for the estimation, to simultaneously exploit the information from sensors and from a mathematical model of the satellite motion. It would be also convenient to adhere to a quaternion representation. This article focuses on some problems linked to this context. The state of the system should be represented in observable form. Singularities due to alignment of measured vectors cause estimation problems. Accommodation of the Kalman filter originates convergence difficulties. The article includes a new proposal that solves these problems, not needing changes in the Kalman filter algorithm. In addition, the article includes assessment of different errors, initialization values for the Kalman filter; and considers the influence of the magnetic dipole moment perturbation, showing how to handle it as part of the Kalman filter framework.
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.
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.
Attitude Estimation of Nano-satellite according to Navigation Sensors using of Combination Method
International Journal of Engineering, 2015
The purpose of this paper is attitude estimation of nano-satellite which requires navigation sensors data for reducing the cost function and movement effect of nano-satellite. The data of navigation sensors and methods are used to achieve the required attitude estimation. The navigation attitude sensors are gyroscope, magnetometer and sun sensor. Furthermore, the extended Kalman filter is used to combine the measured data of gyroscope, magnetometer and sun sensor. This paper presents the methods for accurate estimation of the attitude of nano-satellite missions according to the developed quaternion estimation and nonlinear analysis along with the extended Kalman filter. This work demonstrates the application of nano-satellite with navigation sensors. The methods are used, to achieve high accurate and fast starting attitude estimation. The methods are simulated by MATLAB software. The obtained results were analyzed and compared with other sets of data.
Aerospace
Attitude determination represents a fundamental task for spacecrafts. Achieving this task on small satellites, and nanosatellites, in particular, is further challenging, because the limited power and computational resources available o-board, together with the low development budget, set strict constraints on the selection of the sensors and the complexity of the algorithms. Attitude determination is obtained only from the measurements of a three-axis magnetometer and a model of the Geomagnetic field, stored on the on-board computer. First, the angular rates are estimated and processed using a second-order low-pass Butterworth filter, then they are used as an input, along with Geomagnetic field data, to estimate the attitude matrix using an unsymmetrical TRIAD. The computational efficiency is enhanced by arranging complex matrix operations into a form of the Faddeev algorithm, which is implemented using systolic array architecture on the FPGA core of a CubeSat on-board computer. The...
Satellite orbit and attitude estimation using three-axis magnetometer
The determination of satellite orbital and attitude position and velocity from measurement of a single earth magnetic field (emf) vector without additional measurements, but using a state estimator, is a challenging problem. It is not obvious from first glace whether a solution exists at all – whether the problem is observable with the measurement of only a single emf vector, and an analysis is necessary. This paper performs this analysis for a simple linear system model. Almost circular low earth nearly polar orbits and a dipole emf model are considered. Although these are rather restrictive assumptions they nevertheless provide considerable insight. Both a purely algebraic situation as well as dynamic estimation are studied. It is shown that if the emf induction vector magnitude is used to estimate satellite orbit (position and velocity) and its three projections are used to estimate the attitude, that the situation is sufficiently observable for orbit and attitude determination using just magnetometer measurements. However, for nearly polar orbits, longitude and east velocity are difficult to estimate due to weak observability, and estimation convergence time can be lengthy with poor accuracy. Biographical notes: Y. Kim graduated from the Moscow Aviation Institute as an Electro-Mechanical Engineer specialising in aerospace GN&C and avionics. After graduation he worked in the aerospace industry of the former USSR and various universities as an Engineer, Research Scientist, Professor and Manager, and obtained his PhD and Doctor of Technical Science degrees in aerospace GN&C. After the collapse of the USSR, he worked for IAI in Israel and then for the Canadian Space Agency as an Aerospace and Avionics System Engineer and Scientist. His more recent work has been in satellite control, specifically new methods for attitude and orbit determination and safe hold mode, and lecturing avionics. Hi main areas of interest and contribution are state estimation methods and multisensory navigation systems.
Asian Journal of Control, 2011
In this paper an unscented Kalman filter based procedure for the bias estimation of both the magnetometers and the gyros carried onboard a pico satellite, is proposed. At the initial phase, biases of three orthogonally located magnetometers are estimated as well as the attitude and attitude rates of the satellite. During the initial period after the orbit injection, gyro measurements are accepted as bias free since the precise gyros are working accurately and the accumulated gyro biases are negligible. At the second phase estimated constant magnetometer bias components are taken into account and the algorithm is run for the estimation of the gyro biases that are cumulatively increased by time. As a result, six different bias terms for two different sensors are obtained in two stages, where attitude and attitude rates are estimated regularly. For both estimation phases of the procedure an unscented Kalman filter is used as the estimation algorithm.
Advances in Estimation, Navigation, and Spacecraft Control, 2015
Attitude determination, along with attitude control, is critical to functioning of every space mission. In this paper, we investigate and compare, through simulation, the application of two autonomous sequential attitude estimation algorithms, adopted from the literature, for attitude determination using attitude sensors (sun sensor and horizon sensors) and rate-integrating gyros. The two algorithms include a direction cosine matrix (DCM) based steady-state Kalman Filter and the classic quaternion-based Extended Kalman Filter. To make the analysis realistic, as well as to improve the design of the attitude determination algorithms, detailed sensor measurement models are developed. Modifications in the attitude determination algorithms, through estimation of additional states, to account for sensor biases and misalignments have been presented. A modular six degree-of-freedom closed-loop simulation, developed in house, is used to observe and compare the performances of the attitude determination algorithms.