Satellite Attitude Determination Research Articles

High frequency attitude determination of micro satellites based on FPGA

The accuracy and speed of satellite attitude calculation algorithms are one of the key factors affecting the performance of satellite attitude control. This article proposes a high-performance attitude calculation algorithm for micro satellites based on Field Programmable Gate Array (FPGA). This algorithm is designed and simplified based on the parallel characteristics and abundant computing resources of FPGA. The algorithm fully utilizes the parallel computing power of FPGA and designs a multi-stage pipeline for complex matrix operations in Kalman filtering. It can simultaneously complete data collection and final attitude determination result output, greatly improving the output frequency of attitude information and reducing output delay. The experimental results show that compared to traditional embedded processors, FPGA based attitude calculation has significant advantages in terms of speed.

ROEKF-MPC Estimator for Satellite Attitude and Gyroscope Bias Estimation

Micro-electro-mechanical system (MEMS) based gyroscopes are commonly used for satellite's attitude determination and control system in recent years. They are packaged in a small form factor and have lower power consumption. They provide a low-cost solution to the emerging NewSpace industry. MEMS gyroscopes exhibit time-zero null bias with variation over temperature. To overcome this problem, this paper proposes a technique for self-calibration of gyroscope bias based on reduced order Extended Kalman Filter (EKF) working alongside an estimator based on the model predictive control (MPC) approach. The proposed technique is referred as the ROEKF-MPC Estimator. Both simulation results and experimental verification using an in-house developed spacecraft simulator are presented. Unlike the EKF method, the proposed method can estimate the gyroscope bias instantly and it is robust against changes in temperature. In addition, the proposed method employing a reduced order EKF is 28.4% computationally more efficient than the typical higher order EKF method. Results show that the pointing performance is better than 0.35deg making the proposed method very attractive for most NewSpace applications. While the proposed method allows for self-calibration of gyroscope bias, its performance is affected by the gyroscope noise and requires the satellite's attitude to be nearly in steady state. This limitation will be addressed in detail.

Design and Analysis of AAUSAT Cube Satellite Attitude Determination with PID Algorithms and Orbitron TLE

Satellites are intended to be of massive cost, hard, or hard to keep up and fix in circle. The point of this task is to decide an effective strategy to decide the orbital and heading data for the Nano satellite. To design the exact trajectory and circle for a satellite, information regarding the components which are liable for the deviation in the method of satellites. The factors that should be taken into account to determine the exact location of the satellite. It could be accomplished with the assistance of software responsible for orbit simulation, to do programming with Orbitron so as to get the required output. Orbitron is a simple 2D solver in which TLE files are uploaded, for AAUSAT CUBESAT. The various impacts on the satellite in space are slight deviation in the satellite from its orbit. The motion of the body with includes the disturbing forces in the orbit. However, a satellite has deviated from its normal path due to several forces. This deviation is termed as orbital perturbation. The changes in the orbital element with respect to secular variations are considered using orbital quaternions. The output from satellite dynamic model is received from attitude sensor. The comparative data (Input and Feedback) will generate error signal. To minimize the error signal using proportional integral and derivative (PID) is proposed controller implemented with MATLAB environment.

On-Orbit Magnetometer Data Calibration Using Genetic Algorithm and Interchangeability of the Calibration Parameters

Magnetometers are important sensors with applications in the attitude determination and control systems of satellites. CubeSats have certain limitations related to power, mass, and volume. Due to this, CubeSat magnetometers are not separated from other electrical circuits inside the satellite. Thus, it is important to calibrate the magnetometer, simulating operating conditions while the satellite is running before the launch. However, due to the limited facilities, not every CubeSat is able to calibrate its magnetometers properly on the ground. This study focuses on the calibration of on-orbit magnetometer data observed by BIRDS-3 CubeSats with a genetic algorithm. High oscillations in the total magnetic field were found in the on-orbit magnetic field data measured by magnetometers inside BIRDS-3 CubeSats. Nine unknowns, scaling factors, non-orthogonal angles, and offsets are identified with the genetic algorithm. This paper discusses the factors that affect the high oscillations in the measured total magnetic field data. For the calibration, we used magnetic field data similar to those of a model magnetic field, as the deviation is smaller. This paper presents the accuracy of determining unknowns using the genetic algorithm, as well as the interchangeability of the answers with additional orbit data from the same satellite. This method can be used in the future to calibrate magnetometers inside CubeSats before or after launch.

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.

Optimization of Gradient Descent Parameters in Attitude Estimation Algorithms.

Attitude estimation methods provide modern consumer, industrial, and space systems with an estimate of a body orientation based on noisy sensor measurements. The gradient descent algorithm is one of the most recent methods for optimal attitude estimation, whose iterative nature demands adequate adjustment of the algorithm parameters, which is often overlooked in the literature. Here, we present the effects of the step size, the maximum number of iterations, and the initial quaternion, as well as different propagation methods on the quality of the estimation in noiseless and noisy conditions. A novel figure of merit and termination criterion that defines the algorithm's accuracy is proposed. Furthermore, the guidelines for selecting the optimal set of parameters in order to achieve the highest accuracy of the estimate using the fewest iterations are proposed and verified in simulations and experimentally based on the measurements acquired from an in-house developed model of a satellite attitude determination and control system. The proposed attitude estimation method based on the gradient descent algorithm and complementary filter automatically adjusts the number of iterations with the average below 0.5, reducing the demand on the processing power and energy consumption and causing it to be suitable for low-power applications.

HE[formula omitted]LM-AD: Hierarchical and efficient attitude determination framework with adaptive error compensation module based on ELM network

Satellite attitude determination, as a crucial pre-processing technology in remote sensing earth observation, is significantly associated with the geometric accuracy of satellite stereo images. To compensate for the observation errors in raw attitude data for achieving accurate results, the conventional Kalman filter-based and filter-smoothing combination frameworks provide insufficient access to statistical features of data, resulting in inferior practical system performance. To conquer these drawbacks, a hierarchical and efficient attitude determination framework is proposed called HE2LM-AD, which consists of a simplified adaptive Kalman filtering module, a neural network-based system error compensation module, and a weighted attitude smoothing module. To be more specific, an adaptive noise covariance matrix update is exploited in the Extended Kalman Filter (EKF) iterations, and a simplification scheme based on Cholesky decomposition is employed to obtain coarse filter outputs efficiently. Following that, a dedicated Extreme Learning Machine (ELM)-based error compensation network is designed to capture the pattern of observation errors, providing robust compensation at lower overhead. To further pursue high-performance attitude determination results, we fused the outcomes of the aforementioned two components according to the weights achieved from adaptive learning. Experiment results on in-orbit satellite attitude simulation datasets demonstrate and validate the feasibility and effectiveness of our framework.

Research on the Diagnosability of a Satellite Attitude Determination System on a Fault Information Manifold

In this paper, a new method for fault diagnosability research based on information geometry is proposed. The problem of the diagnosability evaluation of dynamic system faults is transformed into a distance calculation problem on a manifold. The Fisher information distance is used to realize a quantitative judgment of diagnosability, and a quantitative evaluation index of the fault diagnosability of a satellite attitude determination system is designed. This includes a fault detectability index and a fault isolability index. The validity and superiority of the new indexes are verified through a mathematical simulation. In addition, the fault information is visually presented by the geodesics of the fault manifold, and the properties and behavior of the fault are mined and analyzed on the fault information manifold, which lays a foundation for further exploration of fault information through geometric methods.

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.

Low frequency error analysis and calibration for multiple star sensors system of GaoFen7 satellite

ABSTRACT The GaoFen7 (GF7) optical satellite is the first Chinese civilian sub-meter high-resolution stereo mapping satellite and is equipped with a double linear array camera and laser altimeter to achieve large-scale topographic mapping. To improve the accuracy of attitude determination, an attitude determination system comprised of four star sensors is loaded. According to the measurement accuracy and steady performance, the star sensors 1a and 1b is usually used together for satellite attitude calculation, which is called the conventional mode of attitude determination. Then, the combination of star sensors 2a and 2b is called the unconventional mode of attitude determination. Affected by variations in the incident angle of sunlight and solar radiation, thermal deformation occurs in the body and installation structure of the star sensor, which causes Attitude Low-Frequency Error (ALFE) and seriously influences the consistency of attitude determination results of different combination modes for multiple star sensors system. This study proposes an ALFE analysis and calibration approach for the multiple star sensors system of GF7 satellite to ensure the consistency of attitude determination results of different combination modes. Based on the statistical characteristics of the angles of the three axes, the installation parameters of the four star sensors are first calibrated. After analyzing the characteristics of the optical axis angles within 1420 orbit periods over 135 days, the segmented ALFE compensation model between the unconventional and conventional modes is proposed based on the Fourier series model and input parameter of latitude. Based on the on-orbit installation parameters and the ALFE model, the precise attitude determination results of the unconventional mode are calculated. Experimental results show that the attitude determination consistency after compensation is better than 2”. Moreover, the reliable application time range of the compensation model is 30 days to satisfy the requirements for high-precision attitude determination of GF7 satellite.

Redundancy allocation optimizing in the satellite attitude determination and control system based on the exact solution algorithm

The redundancy allocation problem is to find an optimal allocation of redundant components by considering the set of resources and cost constraints. In this research, the satellite attitude determination and control system is studied and its components is introduced, then the reliability of this system is modeled and optimized based on a mathematical approach based on the redundancy allocation. The model studied in this paper is about the structure of a series-parallel system that is in the exact mode of a satellite attitude determination and control system. In this paper, a new approach for modeling and optimization is presented. The mathematical model presented in this paper is into the class of mixed integer non-linear programming (MINLP). Solving these problems is very important for various researchers due to the high mathematical complexity. In this research, a heuristic method is used to the problem exact solution, the results of which have been reported.

Image-based attitude determination of co-orbiting satellites using deep learning technologies

Active debris removal missions pose demanding guidance, navigation and control requirements. We present a novel approach which adopts deep learning technologies to the problem of attitude determination of an uncooperative debris satellite of an a-priori unknown geometry. A siamese convolutional neural network is developed, which detects and tracks inherently useful landmarks from sensor data, after training upon synthetic datasets of visual, LiDAR or RGB-D data. The method is capable of real-time performance while improving upon conventional computer vision-based approaches, and generalises well to previously unseen object geometries, enabling this approach to be a feasible solution for safely performing guidance and navigation in active debris removal, satellite servicing and other close proximity operations. The performance of the algorithm, its sensitivity to model parameters and its robustness to illumination and shadowing conditions, are analysed via numerical simulation.

In-Orbit Attitude Determination of the UVSQ-SAT CubeSat Using TRIAD and MEKF Methods.

Ultraviolet and infrared sensors at high quantum efficiency on-board a small satellite (UVSQ-SAT) is a CubeSat dedicated to the observation of the Earth and the Sun. This satellite has been in orbit since January 2021. It measures the Earth’s outgoing shortwave and longwave radiations. The satellite does not have an active pointing system. To improve the accuracy of the Earth’s radiative measurements and to resolve spatio-temporal fluctuations as much as possible, it is necessary to have a good knowledge of the attitude of the UVSQ-SAT CubeSat. The attitude determination of small satellites remains a challenge, and UVSQ-SAT represents a real and unique example to date for testing and validating different methods to improve the in-orbit attitude determination of a CubeSat. This paper presents the flight results of the UVSQ-SAT’s attitude determination. The Tri-Axial Attitude Determination (TRIAD) method was used, which represents one of the simplest solutions to the spacecraft attitude determination problem. Another method based on the Multiplicative Extended Kalman Filter (MEKF) was used to improve the results obtained with the TRIAD method. In sunlight, the CubeSat attitude is determined at an accuracy better than 3 (at one ) for both methods. During eclipses, the accuracy of the TRIAD method is 14, while it reaches 10 (at one ) for the recursive MEKF method. Many future satellites could benefit from these studies in order to validate methods and configurations before launch.

On the satellite attitude determination using simple environmental models and sensor data

Attitude determination represents a fundamental task for most of the spacecrafts. It relies on three basic aspects: 1) sensors selection, 2) relevant environmental conditions estimation, and 3) algorithms that relate the sensor measurements to the expected conditions in the reference frame. Each one has its own impact on the accuracy that the system can achieve. Besides, two factors stand out above the others in terms of accuracy: 1) sensor quality (calibration, range, etc), and 2) precision of the environmental models. The computation of the satellite attitude needs at least two independent measurements (magnetometers, solar sensors...), whit their corresponding simulated measurements in the reference frame. Nevertheless, the number of measurements can be reduced to one if the satellite attitude is constrained. This paper describes a procedure to calculate satellites’ attitude and the main environmental models used (Earth magnetic model, Sun position model, Albedo model), including orbit propagation. This methodology can be extended to measure the performance of a sensor if the satellite attitude can be derived from other measurements and satellite constrains. The methodology is checked with data from the UPMSat-2 mission (launched in September 2020 within the VEGA VV16 mission). This is a 50-kg satellite designed and developed at the Universidad Politécnica de Madrid (UPM).

Comparison of algorithms for satellite attitude determination using data from visual sensors

Comparison of algorithms for satellite attitude determination using data from visual sensors

Centered error entropy Kalman filter with application to satellite attitude determination

Due to unavoidable factors, heavy-tailed noise appears in satellite attitude estimation. Traditional Kalman filter is prone to performance degradation and even filtering divergence when facing non-Gaussian noise. The existing robust algorithms have limited accuracy. To improve the attitude determination accuracy under non-Gaussian noise, we use the centered error entropy (CEE) criterion to derive a new filter named centered error entropy Kalman filter (CEEKF). CEEKF is formed by maximizing the CEE cost function. In the CEEKF algorithm, the prior state values are transmitted the same as the classical Kalman filter, and the posterior states are calculated by the fixed-point iteration method. The CEE EKF (CEE-EKF) algorithm is also derived to improve filtering accuracy in the case of the nonlinear system. We also give the convergence conditions of the iteration algorithm and the computational complexity analysis of CEEKF. The results of the two simulation examples validate the robustness of the algorithm we presented.

Comparative Analysis of Low Earth Orbit Satellite Attitude Determination using Sensor Integration and Attitude Estimation using Kalman Filter

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.

Different types of star identification algorithms for satellite attitude determination using star sensor

Different types of star identification algorithms for satellite attitude determination using star sensor

Nano satellite attitude determination with randomly delayed measurements

In this paper, a novel algorithm is developed for Nano satellite attitude determination with delayed observations.The particularity of delayed measurements in attitude determination system is analyzed, and then employing a set of Bernoulli distributed random variables to build delayed sensor model. Based on the model, a modified EKF for nonlinear system is deduced by reorganizing the state update equation when data is time stamped or not, which does not increase the dimension of the state or measurement, and avoids the computation of singular matrix inversion. In addition, an adaptive estimation algorithm of measurement noise matrix is combined to reduce the impact of modeling uncertainty. Finally, simulation results demonstrate that the proposed algorithms lead to significantly improved performance compared with the traditional EKF and some delayed filters in the context of networked control systems.

High-Accuracy Guide Star Catalogue Generation with a Machine Learning Classification Algorithm.

In order to detect gravitational waves and characterise their sources, three laser links were constructed with three identical satellites, such that interferometric measurements for scientific experiments can be carried out. The attitude of the spacecraft in the initial phase of laser link docking is provided by a star sensor (SSR) onboard the satellite. If the attitude measurement capacity of the SSR is improved, the efficiency of establishing laser linking can be elevated. An important technology for satellite attitude determination using SSRs is star identification. At present, a guide star catalogue (GSC) is the only basis for realising this. Hence, a method for improving the GSC, in terms of storage, completeness, and uniformity, is studied in this paper. First, the relationship between star numbers in the field of view (FOV) of a staring SSR, together with the noise equivalent angle (NEA) of the SSR—which determines the accuracy of the SSR—is discussed. Then, according to the relationship between the number of stars (NOS) in the FOV, the brightness of the stars, and the size of the FOV, two constraints are used to select stars in the SAO GSC. Finally, the performance of the GSCs generated by Decision Trees (DC), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), the Magnitude Filter Method (MFM), Gradient Boosting (GB), a Neural Network (NN), Random Forest (RF), and Stochastic Gradient Descent (SGD) is assessed. The results show that the GSC generated by the KNN method is better than those of other methods, in terms of storage, uniformity, and completeness. The KNN-generated GSC is suitable for high-accuracy spacecraft applications, such as gravitational detection satellites.