Three-axis Attitude Determination Research Articles

Development and In-Orbit Operation of Deep Learning Attitude Sensor

This study presents in-orbit demonstrations of a deep learning attitude sensor (DLAS) aboard the Japan Aerospace Exploration Agency (JAXA) small-satellite, Rapid Innovative Payload Demonstration Satellite 1. The DLAS is a spaceborne component that consists of star trackers, Earth cameras, and onboard computers. We propose a novel attitude determination method that uses a small camera and image recognition algorithm on board the DLAS to achieve three-axis attitude determination with minimal weight and volume. The DLAS mission further includes a demonstration of a low-cost high-performance star tracker that integrates consumer products. The development of the DLAS began in 2016 when it was adopted as the theme of the JAXA Innovative Satellite Technology Demonstration program. It was launched into a sun-synchronous orbit at an altitude of 500 km in January 2019 for approximately one year. This paper describes the results obtained from the DLAS operation and its future applications. The DLAS demonstrated an Earth camera image identification accuracy of more than 70%. Moreover, the obtained attitude determination accuracy was approximately 2.8 deg for the Earth camera and approximately 14 arcs for the star trackers. The technologies developed in the DLAS have been implemented in a subsequent microsatellite (HIBARI) of the Tokyo Institute of Technology.

Satellite Attitude Determination Using ADS-B Receiver and MEMS Gyro

Automatic dependent surveillance-broadcast (ADS-B) is a very important communication and surveillance technology in air traffic control (ATC). In the future, more and more satellites will carry out ADS-B technology to perform a global coverage. In order to make full use of the resources in the satellite, this paper proposes a solution for satellite three-axis attitude determination using the ADS-B receiver. The principle of ADS-B-based attitude determination is presented first. On this basis, ADS-B-based methods are employed to solve the problem. To achieve a higher attitude determination precision, gyro is combined with the ADS-B receiver using a multiplicative extended Kalman filter (MEKF). Finally, a simulation is carried out and the result is presented.

Design and Verification of an Integrated Panoramic Sun Sensor atop a Small Spherical Satellite.

This paper proposes an integrated panoramic sun sensor (IPSS) for the small spherical satellite Q-SAT that has been working in orbit since 2020. IPSS is essentially a set of temperature-compensated photoelectric cells distributed on the spherical surface of Q-SAT. Compared with traditional sun sensors, IPSS has full spherical coverage of 4π so that the sun vector from any direction can be inversed. The mechatronic design and mathematical model of the proposed IPSS are presented. In-depth error analyses in terms of albedo effect, sampling error, parameter deviation, etc. are carried out. IPSS can provide a sun vector inversion accuracy of 1.5∘ where albedo disturbance does not dominate. Simulation results show that the measurement of IPSS together with a COTS magnetometer can support the three-axis attitude determination of satellites in various orbits and can adapt to the seasonal variations of subpolar points. Ground experimental results and on-orbit data have also verified the feasibility and performance of IPSS. Although the panoramic sun sensor is designed for the small spherical Q-SAT, it can also be applied to other satellites with limited power budgets.

Error Analysis of Accelerometer- and Magnetometer-Based Stationary Alignment

This paper revisits the stationary attitude initialization problem, i.e., the stationary alignment, of Attitude and Heading Reference Systems (AHRSs). A detailed and comprehensive error analysis is proposed for four of the most representative accelerometer- and magnetometer-based stationary attitude determination methods, namely, the Three-Axis Attitude Determination (TRIAD), the QUaternion ESTimator (QUEST), the Factored Quaternion Algorithm (FQA), and the Arc-TANgent (ATAN). For the purpose of the error analysis, constant biases in the accelerometer and magnetometer measurements are considered (encompassing, hence, the effect of hard-iron magnetism), in addition to systematic errors in the local gravity and Earth magnetic field models (flux density magnitude, declination angle, and inclination angle). The contributions of this paper are novel closed-form formulae for the residual errors (normality, orthogonality, and alignment errors) developed in the computed Direction Cosine Matrices (DCM). As a consequence, analytical insight is provided into the problem, allowing us to properly compare the performance of the investigated alignment formulations (in terms of ultimate accuracy), as well as to remove some misleading conclusions reported in previous works. The adequacy of the proposed error analysis is validated through simulation and experimental results.

On-board Relative Attitude Determination and Propagation Using Earth Sensor

In this paper, we propose methods to determine and propagate relative attitude using an earth sensor. Three-axis attitude determination using the Earth sensor is advantageous in terms of space consumption, but it is a fault that we cannot always use it. By detecting and propagating relative attitude information, we attempt to fill that time gaps. The difficulty is that the spacecraft cannot ignore its own translational movement when it looks at the earth, unlike when it looks at the stars. In this research, the attitude and position estimation method used in computer vision is applied instead of the traditional attitude determination method for spacecrafts used in STT and so on. The algorithms use the movement of a point common to two images taken with a time interval, and the translational movement information of the spacecraft obtained from orbit information. The obtained relative attitude is propagated using a Kalman filter to estimate the absolute attitude. The proposed algorithms are evaluated by numerical simulations. The results are as accurate as small MEMS gyro sensors, suggesting the possibility of being replaceable in the future.

Three-Axis Attitude Determination with Pseudo-Bias Estimation from Gravity/Magnetic Vector Observations

This paper revisits the stationary alignment problem of attitude and heading reference systems (AHRSs). The investigation is initiated from the three-axis attitude determination (TRIAD)–based method, purposely chosen to use nonunitary/nonorthogonal observations of the local gravity and Earth’s magnetic flux density vectors. For the simplifying assumption of body and navigation frames aligned, a comprehensive error analysis is developed, providing additional insight into the problem. Closed-form formulas for the residual normality and orthogonality errors are used to devise innovative pseudo-bias estimation (PBE) algorithms for the - and -axis magnetometer biases, as well as for the -axis accelerometer bias. As a direct consequence of applying the latter, and also figuring as the main contribution of this paper, an improved (more accurate) AHRS stationary alignment approach is obtained, herein called TRIAD-PBE. The error analysis is extended to representative state-of-the-art attitude determination techniques, confirming the relevance of TRIAD-PBE, especially in the presence of hard-iron magnetism. Results from simulated and experimental tests confirm the adequacy of the outlined verifications.

Filtering-Based Three-Axis Attitude Determination Package for Spinning Spacecraft: Preliminary Results with Arase

JAXA’s ERG (Exploration of Energization and Radiation in Geospace) Spacecraft, which is nicknamed Arase, was launched on 20 December 2016. Arase is a spin-stabilized and Sun-oriented spacecraft. Its mission is to explore how relativistic electrons in the radiation belts are generated during space storms. Two different on-ground attitude determination algorithms are designed for the mission: A TRIAD-based algorithm that inherits from old missions and a filtering-based new algorithm. This paper, first, discusses the design of the filtering-based attitude determination algorithm, which is mainly based on an Unscented Kalman Filter (UKF), specifically designed for spinning spacecraft (SpinUKF). The SpinUKF uses a newly introduced set of attitude parameters (i.e., spin-parameters) to represent the three-axis attitude of the spacecraft and runs UKF for attitude estimation. The paper then presents the preliminary attitude estimation results for the spacecraft that are obtained after the launch. The results are presented along with the encountered challenges and suggested solutions for them. These preliminary attitude estimation results show that the expected accuracy of the fine attitude estimation for the spacecraft is less than 0.5°.

A Fast In-Field Coarse Alignment and Bias Estimation Method for Stationary Intermediate-Grade IMUs

This paper presents an innovative in-field coarse bias estimation (CBE) method for stationary intermediate-grade inertial measurement units (IMUs), which is based on the existence of normality and orthogonality errors in the attitude matrix supplied by the traditional three-axis attitude determination stationary coarse alignment (CA) stage. Differently from the currently existing in-field bias estimation formulations, the proposed CBE method is conceived to be conducted in stationary conditions, jointly with the CA, and it allows the down accelerometer bias and the north and down angular rate sensor biases to be adequately estimated. As a main contribution of this paper, we demonstrate that the proposed CBE method is able to greatly accelerate the estimation process of the aforementioned biases, contributing, hence, to shorten the IMU setup time. A comprehensive error analysis is presented, and the conclusions are twofold: 1) the proposed CBE method is valid regardless of the IMU orientation and 2) its estimation accuracy is mainly constrained by the north accelerometer bias and the position location uncertainty, which are, in general, very reduced. Results from simulated and experimental test confirm the adequacy of the outlined verifications, also attesting the superiority of the proposed CBE method in situations where, due to time constraints, stationary fine alignment procedures are not allowed to be carried out.

Stabilization and Attitude Determination Methods for FalconSAT-3

FalconSAT-3 is a cadet-built and -operated satellite that was launched in 2007. In late 2014, cadets successfully stabilized the satellite for the first time through an operational test campaign using a -dot controller. This paper describes the many challenges in attitude stabilization and determination, and the solutions provided to achieve partial mission capability of two payloads with a magnetometer as the only sensor. Due to problems with onboard sun sensors, engineers have used solar panel currents to create a sun vector to perform a full three-axis attitude determination. This paper compares two different methods for determining the sun vector from solar panel currents and uses the triad method to perform a three-axis attitude determination. A discussion of the key lessons learned from the cadet-run satellite operations is also included.

3CAT-2: Attitude Determination and Control System for a GNSS-R Earth Observation 6U CubeSat Mission

This work describes the attitude determination and control system (ADCS) of 3CAT-2, a six-unit CubeSat scheduled for launch this 2016. The ADCS of 3CAT-2 aims at controlling the satellite in orbit and fulfilling the pointing requirements imposed by the mission. The attitude control system implemented in 3CAT-2 will be used to point the antennas towards the Earth to perform altimetry tests, orient the solar panels towards the Sun to maximize power input when battery levels are critical, and reduce the tumbling motion of the satellite after the deployment phase. In order to guarantee pointing requirements for remote sensing purposes, an active three-axis attitude determination and control system is considered.

Attitude determination of planetary exploration rovers using solar panels characteristics and accelerometer

In this study, we propose a new attitude determination system, which we call Irradiance-based Attitude Determination (IRAD). IRAD employs the characteristics and geometry of solar panels. First, the sun vector is estimated using data from solar panels including current, voltage, temperature, and the normal vectors of each solar panel. Because these values are obtained using internal sensors, it is easy for rovers to provide redundancy for IRAD. The normal vectors are used to apply to various shapes of rovers. Second, using the gravity vector obtained from an accelerometer, the attitude of a rover is estimated using a three-axis attitude determination method. The effectiveness of IRAD is verified through numerical simulations and experiments that show IRAD can estimate all the attitude angles (roll, pitch, and yaw) within a few degrees of accuracy, which is adequate for planetary explorations.

GPS-based attitude determination for a spinning rocket

An algorithm is developed for determining the attitude of a spinning sounding rocket. This algorithm is able to track global positioning system (GPS) signals with intermittent availability but with enough accuracy to yield phase observables for the precise, three-axis attitude determination of a nutating rocket. Raw, single-frequency GPS RF front-end data are processed by several filters to accomplish this task. First, a Levenberg-Marquardt algorithm (LMA) estimates GPS observables for multiple satellites by performing a least-squares fit to the accumulation outputs of a bank of correlators. These observables are then used as measurements in a Rauch-Tung-Striebel smoother that optimizes estimates of carrier phase, Doppler shift, and code phase. Finally, attitude determination is carried out by another batch filter that uses the single-differenced optimized carrier phase estimates between two antennas and an Euler dynamics model for the torque-free attitude motion of the spinning rocket. This second batch filter implements a combination of a substantially modified form of the LMA and the least-squares ambiguity decorrelation adjustment (LAMBDA) method. This design enables it to deal with integer ambiguities that change over long data gaps between times of carrier phase availability. The algorithm presented in this paper is applied to recorded RF data from a spinning sounding rocket mission to produce attitude quaternion and spin-rate estimates using a pair of antennas separated by a 0.3-m baseline. These results are confirmed by another set of quaternions and spin-rate vectors independently estimated from magnetometer and horizon crossing indicator data. Attitude precision on the order of several degrees has been demonstrated.

UAV Attitude Estimation Using Unscented Kalman Filter and TRIAD

A main problem in autonomous vehicles in general, and in unmanned aerial vehicles (UAVs) in particular, is the determination of the attitude angles. A novel method to estimate these angles using off-the-shelf components is presented. This paper introduces an attitude heading reference system (AHRS) based on the unscented Kalman filter (UKF) using the three-axis attitude determination (TRIAD) algorithm as the observation model. The performance of the method is assessed through simulations and compared to an AHRS based on the extended Kalman filter (EKF). The paper presents field experiment results using a real fixed-wing UAV. The results show good real-time performance with low computational cost in a microcontroller.

Magnetometer-only linear attitude estimation for bias momentum pico-satellite

Satellite attitude information is essential for pico-satellite applications requiring light-weight, low-power, and fast-computation characteristics. The objective of this study is to provide a magnetometer-only attitude estimation method for a low-altitude Earth orbit, bias momentum pico-satellite. Based on two assumptions, the spacecraft spherical symmetry and damping of body rates, a linear kinematics model of a bias momentum satellite’s pitch axis is derived, and the linear estimation algorithm is developed. The algorithm combines the linear Kalman filter (KF) with the classic three-axis attitude determination method (TRIAD). KF is used to estimate satellite’s pitch axis orientation, while TRIAD is used to obtain information concerning the satellite’s three-axis attitude. Simulation tests confirmed that the algorithm is suited to the time-varying model errors resulting from both assumptions. The estimate result keeps tracking satellite attitude motion during all damping, stable, and free rotating control stages. Compared with nonlinear algorithms, such as extended Kalman filer (EKF) and square root unscented Kalman filer (SRUKF), the algorithm presented here has an almost equal performance in terms of convergence time and estimation accuracy, while the consumption of computing resources is much lower.

Three-axis attitude determination using incomplete vector observations

Attitude determination refers to determining the orientation of a vehicle relative to its reference coordinate system. This paper presents a new algorithm for three-axis attitude determination using measurement vectors composed of incomplete vector components. The attitude determination performance of the proposed algorithm is compared with those of the TRIAD and QUEST algorithms in a simulation.

Spacecraft attitude determination by Kalman filtering of Global Positioning System signals

A system utilizing Global Positioning System signals for three-axis attitude determination of a low-Earth-orbiting spacecraft is described. Measurements of wavefront aspect made by observing the phase differences of the same signal received at two separated antennas are processed by a Kalman filter to produce an estimate of vehicle attitude, body rate, disturbance torque, and antenna phase center shift. Factors affecting system accuracy are examined, including global positioning system satellite visibility, number of receiving antennas, and attitude error observability, which is strongly a function of vehicle dynamic motion. The Global Positioning System hardware suite is described as is a typical application to a momentum biased, nadir pointing spacecraft. A high-fidelity computer simulation is used to demonstrate that the global positioning system sensor can lend adequate attitude determination accuracy to this class of missions.

Three-axis attitude determination via Kalman filtering of magnetometer data

A three-axis, magnetometer/Kalman filter, attitude-determination system for a spacecraft in low-altitude Earth orbit is developed, analyzed, and simulation tested. The motivation for developing this system is to achieve three-axis knowledge using magnetic field measurements only. The extended Kalman filter estimates the attitude, attitude rates, and constant disturbance torques. Covariance computation and simulation testing are used to evaluate performance. One test case, a gravity-gradient stabilized spacecraft with a pitch momentum wheel and a magnetically anchored damper, is a real satellite on which this attitude determination system will be used. The application to a nadir-pointing satellite and the estimation of disturbance torques represent the significant extensions contributed by this paper. Beyond its usefulness purely for attitude determination, this system could be used as a part of a low-cost, three-axis attitude stabilization system. I. Introduction T HE objective of this work has been to develop a low-cost system for estimation of three-axis, spacecraft-attitude information based solely on three-axis magnetometer measurements from one satellite orbit. Such a system will be useful for missions that operate in an inclined, low-Earth orbit and require only coarse attitude information. It can also serve as the sensor part of a low-cost, three-axis, closed-loop attitude control system or as a backup attitude estimator. A single three-axis magnetometer measurement can give only two-axes worth of attitude information and no attitude rate or disturbance torque information. Therefore, this attitude determination system must use a sequence of magnetometer measurements. It processes these measurements recursively in a Kalman filter. This paper describes the design, development, analysis, and simulation testing of a Kalman filter arid reports its expected peformance. A follow-on, postlaunch paper is planned to report actual performance.

Three-axis attitude determination from vector observations

Two computationally efficient algorithms are presented for determining three-axis attitude from two or more vector observations. The first of these, the TRIAD algorithm, provides a deterministic (i.e., nonoptimal) solution for the attitude based on two vector observations. The second, the QUEST algorithm, is an optimal algorithm which determines the attitude that achieves the best weighted overlap of an arbitrary number of reference and observation vectors. Analytical expressions are given for the covariance matrices for the two algorithms using a fairly realistic model for the measurement errors. The mathematical relationship of the two algorithms and their relative merits are discussed and numerical examples are given. The advantage of computing the covariance matrix in the body frame rather than in the inertial frame (e.g., in terms of Euler angles) is emphasized. These results are valuable when a single-frame attitude must be computed frequently. They will also be useful to the mission analyst or spacecraft engineer for the evaluation of launch-window constraints or of attitude accuracies for different attitude sensor configurations.

A Least Squares Estimate of Satellite Attitude

A Least Squares Estimate of Satellite Attitude