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At least 37 records · Page 2

Use of Faraday-rotation data from beacon satellites to determine ionospheric corrections for interplanetary spacecraft navigation

Faraday-rotation data from the linearly polarized 137-MHz beacons of the ATS-1, SIRIO, and Kiku-2 geosynchronous satellites are used to determine the ionospheric corrections to the range and Doppler data for interplanetary spacecraft navigation. The JPL operates the Deep Space Network of tracking stations for NASA; these stations monitor Faraday rotation with dual orthogonal, linearly polarized antennas, Teledyne polarization tracking receivers, analog-to-digital converter/scanners, and other support equipment. Computer software examines the Faraday data, resolves the pi ambiguities, constructs a continuous Faraday-rotation profile and converts the profile to columnar zenith total electron content at the ionospheric reference point; a second program computes the line-of-sight ionospheric correction for each pass of the spacecraft over each tracking complex. Line-of-sight ionospheric electron content using mapped Faraday-rotation data is compared with that using dispersive Doppler data from the Voyager spacecraft; a difference of about 0.4 meters, or 5 x 10 to the 16th electrons/sq m is obtained. The technique of determining the electron content of interplanetary plasma by subtraction of the ionospheric contribution is demonstrated on the plasma torus surrounding the orbit of Io.

Royden, H. N.↗

A Self-Tuning Kalman Filter for Autonomous Spacecraft Navigation

Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman Filter and Global Positioning System (GPS) data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. Current techniques of Kalman filtering, however, still rely on manual tuning from analysts, and cannot help in optimizing autonomy without compromising accuracy and performance. This paper presents an approach to produce a high accuracy autonomous navigation system fully integrated with the flight system. The resulting system performs real-time state estimation by using an Extended Kalman Filter (EKF) implemented with high-fidelity state dynamics model, as does the GPS Enhanced Orbit Determination Experiment (GEODE) system developed by the NASA Goddard Space Flight Center. Augmented to the EKF is a sophisticated neural-fuzzy system, which combines the explicit knowledge representation of fuzzy logic with the learning power of neural networks. The fuzzy-neural system performs most of the self-tuning capability and helps the navigation system recover from estimation errors. The core requirement is a method of state estimation that handles uncertainties robustly, capable of identifying estimation problems, flexible enough to make decisions and adjustments to recover from these problems, and compact enough to run on flight hardware. The resulting system can be extended to support geosynchronous spacecraft and high-eccentricity orbits. Mathematical methodology, systems and operations concepts, and implementation of a system prototype are presented in this paper. Results from the use of the prototype to evaluate optimal control algorithms implemented are discussed. Test data and major control issues (e.g., how to define specific roles for fuzzy logic to support the self-learning capability) are also discussed. In addition, architecture of a complete end-to-end candidate flight system that provides navigation with highly autonomous control using data from GPS is presented.

Truong, Son H.↗

StarNAV: An Architecture for Autonomous Spacecraft Navigation by the Relativistic Perturbation of Starlight

Exploration missions to the outer Solar System (e.g., Neptune, Kuiper belt objects) or to the interstellar medium present several challenges for conventional spacecraft designs. One of the greatest challenges is a means for navigation, as Earth-based tracking with the Deep Space Network (DSN) becomes less desirable due to high cost, decreased performance at long ranges, and long light-time delays. Indeed, light time delays at Neptune are over four hours (one way), making control of spacecraft during critical events nearly impossible—and this problem only becomes worse as we move to the Kuiper belt or to interstellar space. The need for autonomous spacecraft navigation is well established and is prominently featured in the 2015 NASA Technology Roadmap (e.g., TA 5.4.2.6 & 5.4.2.8). This has led to investment in various technologies to accomplish this task, with the majority of recent work focusing on optical navigation (OPNAV; TA 5.4.4.1) [1], X-ray pulsar navigation (XNAV; TA 5.6.1) [2], or DSN one-way ranging with the Deep Space Atomic Clock (DSAC; TA 5.4.1.1) [3]. This NIAC study suggests a new (and completely different) way of autonomously navigating a spacecraft anywhere in the Solar System or beyond. We call this new technique StarNAV.

NIAC Phase I↗

Science Benefits of Onboard Spacecraft Navigation

Primitive bodies (asteroids and comets), which have remained relatively unaltered since their formation, are important targets for scientific missions that seek to understand the evolution of the solar system. Often the first step is to fly by these bodies with robotic spacecraft. The key to maximizing data returns from these flybys is to determine the spacecraft trajectory relative to the target body-in short, navigate the spacecraft- with sufficient accuracy so that the target is guaranteed to be in the instruments' field of view. The most powerful navigation data in these scenarios are images taken by the spacecraft of the target against a known star field (onboard astrometry). Traditionally, the relative trajectory of the spacecraft must be estimated hours to days in advance using images collected by the spacecraft. This is because of (1)!the long round-trip light times between the spacecraft and the Earth and (2)!the time needed to downlink and process navigation data on the ground, make decisions based on the result, and build and uplink instrument pointing sequences from the results. The light time and processing time compromise navigation accuracy considerably, because there is not enough time to use more accurate data collected closer to the target-such data are more accurate because the angular capability of the onboard astrometry is essentially constant as the distance to the target decreases, resulting in better "plane-of- sky" knowledge of the target. Excellent examples of these timing limitations are high-speed comet encounters. Comets are difficult to observe up close; their orbits often limit scientists to brief, rapid flybys, and their coma further restricts viewers from seeing the nucleus in any detail, unless they can view the nucleus at close range. Comet nuclei details are typically discernable for much shorter durations than the roundtrip light time to Earth, so robotic spacecraft must be able to perform onboard navigation. This onboard navigation can be accomplished through a self- contained system that by eliminating light time restrictions dramatically improves the relative trajectory knowledge and control and subsequently increases the amount of quality data collected. Flybys are one-time events, so the system's underlying algorithms and software must be extremely robust. The autonomous software must also be able to cope with the unknown size, shape, and orientation of the previously unseen comet nucleus. Furthermore, algorithms must be reliable in the presence of imperfections and/or damage to onboard cameras accrued after many years of deep-space operations. The AutoNav operational flight software packages, developed by scientists at the Jet Propulsion Laboratory (JPL) under contract with NASA, meet all these requirements. They have been directly responsible for the successful encounters on all of NASA's close-up comet-imaging missions (see Figure !1). AutoNav is the only system to date that has autonomously tracked comet nuclei during encounters and performed autonomous interplanetary navigation. AutoNav has enabled five cometary flyby missions (Table!1) residing on four NASA spacecraft provided by three different spacecraft builders. Using this software, missions were able to process a combined total of nearly 1000 images previously unseen by humans. By eliminating the need to navigate spacecraft from Earth, the accuracy gained by AutoNav during flybys compared to ground-based navigation is about 1!order of magnitude in targeting and 2!orders of magnitude in time of flight. These benefits ensure that pointing errors do not compromise data gathered during flybys. In addition, these benefits can be applied to flybys of other solar system objects, flybys at much slower relative velocities, mosaic imaging campaigns, and other proximity activities (e.g., orbiting, hovering, and descent/ascent).

Autonomy↗

Operational considerations of using GPS for spacecraft navigation

The Flight Dynamics Facility (FDF) at the NASA Goddard Space Flight Center (GSFC) has provided operational spacecraft orbit support for many years, currently generating orbit products for about 20 satellites. To date, operational orbit determination in the FDF has been performed on the ground using data from ground-based or space-based tracking systems. Current development of spaceborne Global Positioning System (GPS) receivers is projected to have a significant effect on the support needed for operational satellite navigation. This paper identifies the functions performed in spacecraft navigation and examines and quantifies how the functions and support levels will be affected as onboard GPS receivers are implemented on spacecraft. Cases are considered spacecraft using or not using NASA ground and space networks resources.

Hart, Roger C.↗

Optical landmark detection for spacecraft navigation

In this paper, a new crater detection algorithm is suggested. Experimental studies show that this new algorithm can achieve sub-pixel accuracy in position, its detection rate is better than 90% and its false alarm rate is less than 5%. These good characteristics indicate that it is an ideal crater detection algorithm for spacecraft optical navigation.

optical↗

Effects of Optical Artifacts in a Laser-Based Spacecraft Navigation Sensor

Testing of the Advanced Video Guidance Sensor (AVGS) used for proximity operations navigation on the Orbital Express ASTRO spacecraft exposed several unanticipated imaging system artifacts and aberrations that required correction to meet critical navigation performance requirements. Mitigation actions are described for a number of system error sources, including lens aberration, optical train misalignment, laser speckle, target image defects, and detector nonlinearity/noise characteristics. Sensor test requirements and protocols are described, along with a summary of test results from sensor confidence tests and system performance testing.

LeCroy, Jerry E.↗

Effects of Optical Artifacts in a Laser-Based Spacecraft Navigation Sensor

Testing of the Advanced Video Guidance Sensor (AVGS) used for proximity operations navigation on the Orbital Express ASTRO spacecraft exposed several unanticipated imaging system artifacts and aberrations that required correction to meet critical navigation performance requirements. Mitigation actions are described for a number of system error sources, including lens aberration, optical train misalignment, laser speckle, target image defects, and detector nonlinearity/noise characteristics. Sensor test requirements and protocols are described, along with a summary of test results from sensor confidence tests and system performance testing.

LeCroy, Jerry E.↗

Effects of Optical Artifacts in a Laser-Based Spacecraft Navigation Sensor

Testing Of the Advanced Video Guidance Sensor (AVGS) used for proximity operations navigation on the Orbital Express ASTRO spacecraft exposed several unanticipated imaging system artifacts and aberrations that required correction, to meet critical navigation performance requirements. Mitigation actions are described for a number of system error sources, including lens aberration, optical train misalignment, laser speckle, target image defects, and detector nonlinearity/noise characteristics. Sensor test requirements and protocols are described, along with a summary ,of test results from sensor confidence tests and system performance testing.

LeCroy, Jerry E.↗

Reinforcement Learning for Spacecraft Navigation & Environment Characterization in the Planar-Restricted Two-Body Problem

During mission planning and execution, spacecraft operators must balance data collection and downlink, systems constraints, human factors, and navigation. As missions become increasingly complex and ambitious, these factors become more intricately entwined and conflicted. For example, a spacecraft’s position must be known accurately in order to point to and image a target. Large position errors may cause missed observations or require additional scanning that increases operations complexity and data volume. Some observations require imaging from specific relative geometries which adds orbit control and timing considerations. Adjusting the orbit may allow for optimal observability of environmental parameters and/or enable more efficient sensor coverage, but maneuver execution error adds uncertainty to the current state which impacts both characterization and coverage objectives.

Navigation↗

Differential Interferometry For Spacecraft Navigation

Report proposes use of differential very-long-baseline interferometry to measure accurately angular separation, viewed from Earth, of two spacecraft near another planet. One spacecraft would be in orbit around planet on trajectory already determined accurately, relative to planet, by conventional Doppler tracking. Other spacecraft would be approaching planet. Use of differential very-long-baseline interferometry "tie" between two spacecraft and previously determined "tie" between orbiter and planet increases accuracy of navigation of approaching spacecraft relative to planet.

Edwards, Charles D.↗

The Global Positioning System and its application in spacecraft navigation

The paper presents an overview of the Global Positioning System (GPS) as well as a discussion of the user system parameters govering the design of a low-earth-orbit spacecraft GPS navigation system. A specific application, the Space Shuttle orbiter GPS navigation system, is discussed with particular attention given to its receiver/processor.

Van Leeuwen, A.↗

Automated landmark identification for spacecraft navigation

An integrated approach to surface relative optical landmark tracking for spacecraft is being developed. Landmarks are defined as full digital topography/albedo maps and are determined from previous imaging and navigation data. Initially, this technique will speed up ground based optical navigation. Ultimately, it will enable on-board trajectory determination during orbital and landing maneuvers.

landmark↗

CCD sensors for interplanetary spacecraft navigation and pointing control

The applications of practical optical image sensing CCDs to interplanetary spacecraft precision science payload delivery are discussed. The increasing demand for precision and flexibility in spacecraft for proposed new missions is forcing a transition to autonomous onboard navigation and pointing control. Onboard processing of video data is a crucial element in this transition and is made possible by CCD imagers and microprocessors. The pending Halley's comet close flyby is used as an example to examine details of the sensor design and the data processing which will meet the requirements. Target body centerfinding for optical navigation, target body tracking signal processing, and CCD sensor heads are examined, and flow charts and block diagrams are given for each.

Eisenman, A. R.↗

Error Analysis System for Spacecraft Navigation Using the Global Positioning System (GPS)

The Flight Dynamics Division (FDD) at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) is currently developing improved space-navigation filtering algorithms to use the Global Positioning System (GPS) for autonomous real-time onboard orbit determination. In connection with a GPS technology demonstration on the Small Satellite Technology Initiative (SSTI)/Lewis spacecraft, FDD analysts and programmers have teamed with the GSFC Guidance, Navigation, and Control Branch to develop the GPS Enhanced Orbit Determination Experiment (GEODE) system. The GEODE system consists of a Kalman filter operating as a navigation tool for estimating the position, velocity, and additional states required to accurately navigate the orbiting Lewis spacecraft by using astrodynamic modeling and GPS measurements from the receiver. A parallel effort at the FDD is the development of a GPS Error Analysis System (GEAS) that will be used to analyze and improve navigation filtering algorithms during development phases and during in-flight calibration. For GEAS, the Kalman filter theory is extended to estimate the errors in position, velocity, and other error states of interest. The estimation of errors in physical variables at regular intervals will allow the time, cause, and effect of navigation system weaknesses to be identified. In addition, by modeling a sufficient set of navigation system errors, a system failure that causes an observed error anomaly can be traced and accounted for. The GEAS software is formulated using Object Oriented Design (OOD) techniques implemented in the C++ programming language on a Sun SPARC workstation. The Phase 1 of this effort is the development of a basic system to be used to evaluate navigation algorithms implemented in the GEODE system. This paper presents the GEAS mathematical methodology, systems and operations concepts, and software design and implementation. Results from the use of the basic system to evaluate navigation algorithms implemented on GEODE are also discussed. In addition, recommendations for generalization of GEAS functions and for new techniques to optimize the accuracy and control of the GPS autonomous onboard navigation are presented.

Truong, S. H.↗