7,806 research outputs found

    Algorithms for autonomous star identification

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    Algorithms for onboard autonomous star identification are presented. The algorithms are applicable to two types of spacecraft missions, those flown with nearly inertially fixed attitude (solar maximum mission type); and those flown with smoothly time varying attitude (LANDSAT-D type)

    Optimization of star research algorithm for esmo star tracker

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    This paper explains in detail the design and the development of a software research star algorithm, embedded on a star tracker, by the ISAE/SUPAERO team. This research algorithm is inspired by musical techniques. This work will be carried out as part of the ESMO (European Student Moon Orbiter) project by different teams of students and professors from ISAE/SUPAERO (Institut Supe ́rieur de l’Ae ́ronautique et de l’Espace). Till today, the system engineering studies have been completed and the work that will be presented will concern the algorithmic and the embedded software development. The physical architecture of the sensor relies on APS 750 developed by the CIMI laboratory of ISAE/SUPAERO. First, a star research algorithm based on the image acquired in lost-in-space mode (one of the star tracker opera- tional modes) will be presented; it is inspired by techniques of musical recognition with the help of the correlation of digital signature (hash) with those stored in databases. The musical recognition principle is based on finger- printing, i.e. the extraction of points of interest in the studied signal. In the musical context, the signal spectrogram is used to identify these points. Applying this technique in image processing domain requires an equivalent tool to spectrogram. Those points of interest create a hash and are used to efficiently search within the database pre- viously sorted in order to be compared. The main goals of this research algorithm are to minimise the number of steps in the computations in order to deliver information at a higher frequency and to increase the computation robustness against the different possible disturbances

    Feasibility study for a scanning celestial attitude determination system SCADS on the IMP spacecraft Final report

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    System design analysis to establish feasibility of using electro-optical celestial scanning sensor on IMP spacecraft for determination of spacecraft attitude by star measurement

    Analysis of Star Identification Algorithms due to Uncompensated Spatial Distortion

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    With the evolution of spacecraft systems, we see the growing need for smaller, more affordable, and robust spacecrafts that can be jettisoned with ease and sent to sites to perform a myriad of operations that a larger craft would prohibit, or that can be quickly manipulated from performing one task into another. The developing requirements have led to the creation of Nano-Satellites, or CubeSats. The question then remains, how to navigate the expanse of space with such a minute spacecraft? A solution to this is using the stars themselves as a means of navigation. This can be accomplished by measuring the distance between stars in a camera image and determining the stars\u27 identities. Once identified, the spacecraft can obtain its position and facing. A series of star identification algorithms called Lost in Space Algorithms (LISAs) are used to recognize the stars in an image and assess the accuracy and error associated with each algorithm. This is done by creating various images from a simulated camera, using a program called MATLAB, along with images of actual stars with uncompensated errors. It is shown how suitable these algorithms are for use in space navigation, what constraints and impediments each have, and if low quality cameras using these algorithms can solve the Lost in Space problem

    Star Imager For Nanosatellite Applications

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    This research examines the feasibility of Commercial-off-the-shelf Complementary Metal-Oxide-Semiconductor image sensors for use on nanosatellites as a star imager. An emphasis is placed on method selection and implementation of the star imager algorithm: Centroiding, Identification and Attitude Determination. The star imager algorithm makes use of the Lost-in-Space condition to provide attitude knowledge for each image. Flat Field, Checker Board and Point Spread Function calibration methods were employed to characterize the star imager. Finally, feasibility testing of the star imager is accomplished through simulations and night sky images

    Development and Initial On-orbit Performance of Multi-Functional Attitude Sensor using Image Recognition

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    This paper describes a multi-functional attitude sensor mounted on the “Innovative Satellite 1st” led by Japan Aerospace Exploration Agency which was launched in January 2019. In order to achieve the high accuracy determination in low cost, we developed a novel attitude sensor utilizing real-time image recognition technology, named “Deep Learning Attitude Sensor (DLAS)”. DLAS has two type of attitude sensors: Star Tracker(STT) and Earth Camera (ECAM). For the low-cost development, we adopted commercial off-the-shelf cameras. DLAS uses real-time image recognition technology and a new attitude determination algorithm. In this paper, we present the missions, methods and system configuration of DLAS and initial results of on-orbit experiment that was conducted after the middle of February 2019, and it is confirmed that attitude determinations using ECAM and STT are performed correctly

    Non-dimensional Star-Identification

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    This study introduces a new "Non-Dimensional" star identification algorithm to reliably identify the stars observed by a wide field-of-view star tracker when the focal length and optical axis offset values are known with poor accuracy. This algorithm is particularly suited to complement nominal lost-in-space algorithms, which may identify stars incorrectly when the focal length and/or optical axis offset deviate from their nominal operational ranges. These deviations may be caused, for example, by launch vibrations or thermal variations in orbit. The algorithm performance is compared in terms of accuracy, speed, and robustness to the Pyramid algorithm. These comparisons highlight the clear advantages that a combined approach of these methodologies provides.Comment: 17 pages, 10 figures, 4 table

    ROSIA: Rotation-Search-Based Star Identification Algorithm

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    This paper presents a rotation-search-based approach for addressing the star identification (Star-ID) problem. The proposed algorithm, ROSIA, is a heuristics-free algorithm that seeks the optimal rotation that maximally aligns the input and catalog stars in their respective coordinates. ROSIA searches the rotation space systematically with the Branch-and-Bound (BnB) method. Crucially affecting the runtime feasibility of ROSIA is the upper bound function that prioritizes the search space. In this paper, we make a theoretical contribution by proposing a tight (provable) upper bound function that enables a 400x speed-up compared to an existing formulation. Coupling the bounding function with an efficient evaluation scheme that leverages stereographic projection and the R-tree data structure, ROSIA achieves feasible operational speed on embedded processors with state-of-the-art performances under different sources of noise. The source code of ROSIA is available at https://github.com/ckchng/ROSIA.Comment: 21 pages, 16 figures, Accepted to IEEE Transactions on Aerospace and Electronic System

    Development of a direct match technique for star identification on the SWAS mission

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    A direct match technique for star identification was developed for use with the star tracker on the SWAS (Submillimeter Wave Astronomy Satellite) spacecraft. In this technique, tracker searches are used in a two-step process for an implicit direct match star identification. A simulation of the star acquisition process was created and used in the preparation of guide star selection requirements. Flight software implementing this star acquisition technique has been developed and tested
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