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NASA NTRS2016

Exploiting Measurement Uncertainty Estimation in Evaluation of GOES-R ABI Image Navigation Accuracy Using Image Registration Techniques

In evaluating GOES-R Advanced Baseline Imager (ABI) image navigation quality, upsampled sub-images of ABI images are translated against downsampled Landsat 8 images of localized, high contrast earth scenes to determine the translations in the East-West and North-South directions that provide maximum correlation. The native Landsat resolution is much finer than that of ABI, and Landsat navigation accuracy is much better than ABI required navigation accuracy and expected performance. Therefore, Landsat images are considered to provide ground truth for comparison with ABI images, and the translations of ABI sub-images that produce maximum correlation with Landsat localized images are interpreted as ABI navigation errors. The measured local navigation errors from registration of numerous sub-images with the Landsat images are averaged to provide a statistically reliable measurement of the overall navigation error of the ABI image. The dispersion of the local navigation errors is also of great interest, since ABI navigation requirements are specified as bounds on the 99.73rd percentile of the magnitudes of per pixel navigation errors. However, the measurement uncertainty inherent in the use of image registration techniques tends to broaden the dispersion in measured local navigation errors, masking the true navigation performance of the ABI system. We have devised a novel and simple method for estimating the magnitude of the measurement uncertainty in registration error for any pair of images of the same earth scene. We use these measurement uncertainty estimates to filter out the higher quality measurements of local navigation error for inclusion in statistics. In so doing, we substantially reduce the dispersion in measured local navigation errors, thereby better approximating the true navigation performance of the ABI system.

navigation performance↗

NASA NTRS1989

GOES I/M image navigation and registration

Image Navigation and Registration (INR) is the system that will be used on future Geostationary Operational Environmental Satellite (GOES) missions to locate and register radiometric imagery data. It consists of a semiclosed loop system with a ground-based segment that generates coefficients to perform image motion compensation (IMC). The IMC coefficients are uplinked to the satellite-based segment, where they are used to adjust the displacement of the imagery data due to movement of the imaging instrument line-of-sight. The flight dynamics aspects of the INR system is discussed in terms of the attitude and orbit determination, attitude pointing, and attitude and orbit control needed to perform INR. The modeling used in the determination of orbit and attitude is discussed, along with the method of on-orbit control used in the INR system, and various factors that affect stability. Also discussed are potential error sources inherent in the INR system and the operational methods of compensating for these errors.

Fiorello, J. L., Jr.↗

NASA NTRS2021

Concept of Operations for OSIRIS-REx Optical Navigation Image Planning

Optical navigation (OpNav) is a critical subsystem of the OSIRIS-REx asteroid sample return mission, which operated in the vicinity of near-Earth asteroid (101955) Bennu from August 2018 through April 2021. A substantial amount of mission resources across multiple subsystems and institutions is required to ensure that the OpNav data are successfully acquired. The KinetX OpNav team, part of the Flight Dynamics System (FDS), is responsible for performing required analysis to develop the OpNav operations plans; requesting, reviewing and verifying the plans; and ultimately using the image data for critical navigation operations. The FDS team, responsible for the mission navigation, is operated by KinetX Aerospace with management and operations support from NASA’s Goddard Space Flight Center. The Science Processing and Operations Center (SPOC), located at the University of Arizona’s Lunar and Planetary Laboratory, is responsible for generating the planning products for all science and most OpNav data. These plans are integrated into the spacecraft sequences, tested, and commanded by the Mission Support Area (MSA) at Lockheed Martin Space. To ensure mission-critical navigation image data are successfully acquired, the plan is developed through a waterfall of planning cycles over the course of 3 months prior to onboard plan execution. During the initial strategic planning for a mission phase, detailed analysis is performed by the OpNav team to conceptualize the concept of operations (ConOps) for image data collection. This phase OpNav Narrative is included along with other strategic planning documents for the key ground segment stakeholders to review and provide feedback. The detailed OpNav plans get defined in the tactical planning cycle, which spans 8 to 3 weeks before the week-long integrated sequence is executed on-board the spacecraft. During the tactical cycle, the initial OpNav Request is submitted along with the science requests, kicking off development of the science and OpNav plans. Once the initial plan is drafted, interfaces are exercised so that the plan can be reviewed and iterated, if necessary. A rigorous schedule is followed by the planning teams during the implementation cycle, spanning the last 18 days before uplink, to ensure all the necessary integration, testing, and reviewing can occur on time. The development of the OpNav planning ConOps, including responsibilities, interfaces, timelines, and procedures, took extensive collaboration across mission elements and institutions. The process was robust throughout the 137 weeks of continuous Optical Navigation Operations at Bennu, which concluded on April 9th, 2021.

Coralie D. Adam↗

NASA NTRS2016

Image Navigation and Registration Performance Assessment Tool Set for the GOES-R Advanced Baseline Imager and Geostationary Lightning Mapper

The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. For ABI, these metrics are the 3-sigma errors in navigation (NAV), channel-to-channel registration (CCR), frame-to-frame registration (FFR), swath-to-swath registration (SSR), and within frame registration (WIFR) for the Level 1B image products. For GLM, the single metric of interest is the 3-sigma error in the navigation of background images (GLM NAV) used by the system to navigate lightning strikes. 3-sigma errors are estimates of the 99.73rd percentile of the errors accumulated over a 24-hour data collection period. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24-hour evaluation period. Another aspect of the IPATS design that vastly reduces execution time is the off-line propagation of Landsat based truth images to the fixed grid coordinates system for each of the three GOES-R satellite locations, operational East and West and initial checkout locations. This paper describes the algorithmic design and implementation of IPATS and provides preliminary test results.

Image Navigation↗

NASA NTRS2016

Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for the GOES-R Advanced Baseline Imager and Geostationary Lightning Mapper

The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. For ABI, these metrics are the 3-sigma errors in navigation (NAV), channel-to-channel registration (CCR), frame-to-frame registration (FFR), swath-to-swath registration (SSR), and within frame registration (WIFR) for the Level 1B image products. For GLM, the single metric of interest is the 3-sigma error in the navigation of background images (GLM NAV) used by the system to navigate lightning strikes. 3-sigma errors are estimates of the 99.73rd percentile of the errors accumulated over a 24 hour data collection period. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24 hour evaluation period. Another aspect of the IPATS design that vastly reduces execution time is the off-line propagation of Landsat based truth images to the fixed grid coordinates system for each of the three GOES-R satellite locations, operational East and West and initial checkout locations. This paper describes the algorithmic design and implementation of IPATS and provides preliminary test results.

Image Registration↗

NASA NTRS1990

The near real time image navigation of pictures returned by Voyager 2 at Neptune

The development of a process for performing image navigation in near real time is described. The process was used to accurately determine the camera pointing for pictures returned by the Voyager 2 spacecraft at Neptune Encounter. Image navigation improves knowledge of the pointing of an imaging instrument at a particular epoch by correlating the spacecraft-relative locations of target bodies in inertial space with the locations of their images in a picture taken at that epoch. More than 8,500 pictures returned by Voyager 2 at Neptune were processed in near real time. The results were used in several applications, including improving pointing knowledge for nonimaging instruments ('C-smithing'), making 'Neptune, the Movie', and providing immediate access to geometrical quantities similar to those traditionally supplied in the Supplementary Experiment Data Record.

Underwood, Ian M.↗

NASA NTRS1992

Automatic AVHRR image navigation software

This is the final report describing the work done on the project entitled Automatic AVHRR Image Navigation Software funded through NASA-Washington, award NAGW-3224, Account 153-7529. At the onset of this project, we had developed image navigation software capable of producing geo-registered images from AVHRR data. The registrations were highly accurate but required a priori knowledge of the spacecraft's axes alignment deviations, commonly known as attitude. The three angles needed to describe the attitude are called roll, pitch, and yaw, and are the components of the deviations in the along scan, along track and about center directions. The inclusion of the attitude corrections in the navigation software results in highly accurate georegistrations, however, the computation of the angles is very tedious and involves human interpretation for several steps. The technique also requires easily identifiable ground features which may not be available due to cloud cover or for ocean data. The current project was motivated by the need for a navigation system which was automatic and did not require human intervention or ground control points. The first step in creating such a system must be the ability to parameterize the spacecraft's attitude. The immediate goal of this project was to study the attitude fluctuations and determine if they displayed any systematic behavior which could be modeled or parameterized. We chose a period in 1991-1992 to study the attitude of the NOAA 11 spacecraft using data from the Tiros receiving station at the Colorado Center for Astrodynamic Research (CCAR) at the University of Colorado.

Baldwin, Dan↗

NASA NTRS1989

AVHRR image navigation - Summary and review

The navigation of imagery from polar orbiting weather satellites includes the correction for geometric distortions due to earth shape/earth rotation, satellite orbit variations, and satellite attitude along with the resampling of the satellite image to a selected geographic map projection. The routine image navigation procedure also compensates for the distortion of the satellite imagery due to the nonlinear scanning of the sensor system. This paper reviews general methods for performing this image navigation, ranging from a method that assumes no orbital information and, thus, relies on nominal orbital parameter values and image corrections computed by matching ground control points (GCPs), to a method that uses high-quality satellite ephemeris data to make the correction with a limited number of GCPs. A procedure to optimize the image navigation by using a spatial remapping, or interpolation, is introduced and outlined. Recommendations are made for people interested in the processing of AVHRR imagery.

Emery, William J.↗

NASA NTRS2008

Effect of GOES-R Image Navigation and Registration Errors on Atmospheric Motion Vectors

High temporal frequency imagery from geostationary satellites allows for the continuous monitoring of rapidly changing atmospheric constituents such as smoke, dust, water vapor and clouds. The image sequences are often used to quantify the displacement of image features such as water vapor and clouds to produce atmospheric motion vectors (AMVs) which are used as diagnostic tools and also assimilated into numerical weather forecast models. The basic principle behind the determination of AMVs is the calculation of the physical displacement of features from one image (time) to the next. This process assumes that the features being tracked do not change as a function of time, usually requiring the use of short time interval imagery to minimize substantial change in size and shape of the features being tracked. High spatial resolution imagery also is required for reliable feature identification. While these image resolution and temporal sampling requirements often provide major drivers for space-based instrument design requirements, accurate image navigation and registration, INn (between a sequence of images), is also critical to the derivation of useful AMVs. In this paper and poster to be presented at the conference, the image navigation and registration (INR) accuracy expected for the Advanced Baseline Imager (ABI) on the GOES-R series of satellites will be discussed in light of its impact on AMV accuracy. Significant satellite platform and modeling enhancements are planned which should significantly improve INn performance of the GOES-R instruments. Some of these improvements have been demonstrated for the GOES-13 satellite which was launched in summer of 2006. An analysis of GOES-13 INR data, from the special satellite check out period, will be used in the assessment.

Jedlovec, Gary↗

NASA NTRS1990

Simulation studies of the GOES-I Image Navigation and Registration (INR) system

The GOES-I Image Navigation and Registration (INR) system will fly on the next generation of NOAA geostationary meteorological satellites. This system uses observations of stars and earth landmarks made by the Imager and Sounder Instruments themselves to determine the orbit, attitude, and misalignment state of the spacecraft and instruments. An extensive program of simulation and analysis has been conducted to assess the expected performance of the INR system. This paper discusses these activities and presents some computer simulation results showing expected system performance under a variety of conditions including station-keeping.

Carr, James L.↗

NASA NTRS2017

Image Navigation and Registration Performance Assessment Evaluation Tools for GOES-R ABI and GLM

Image registration↗

NASA NTRS2018

Testing of the Orion Optical Navigation Image Processing Algorithms

The Optical Navigation System for Orion has been developed to ensure that the crew can return safely to Earth in case of a loss of communications. This presentation contains an overview of the image processing aspect of the Orion optical navigation system. The top level architecture is presented along with results of the testing of these image processing system.

Optical Navigation↗

NASA NTRS2017

Image Navigation and Registration Performance Assessment Evaluation Tools for GOES-R ABI and GLM

image registration↗

NASA NTRS2007

Measuring Image Navigation and Registration Performance at the 3-Sigma Level Using Platinum Quality Landmarks

Geostationary Operational Environmental Satellite (GOES) Image Navigation and Registration (INR) performance is specified at the 3- level, meaning that 99.7% of a collection of individual measurements must comply with specification thresholds. Landmarks are measured by the Replacement Product Monitor (RPM), part of the operational GOES ground system, to assess INR performance and to close the INR loop. The RPM automatically discriminates between valid and invalid measurements enabling it to run without human supervision. In general, this screening is reliable, but a small population of invalid measurements will be falsely identified as valid. Even a small population of invalid measurements can create problems when assessing performance at the 3-sigma level. This paper describes an additional layer of quality control whereby landmarks of the highest quality ("platinum") are identified by their self-consistency. The platinum screening criteria are not simple statistical outlier tests against sigma values in populations of INR errors. In-orbit INR performance metrics for GOES-12 and GOES-13 are presented using the platinum landmark methodology.

Carr, James L.↗

NASA NTRS1988

Star sightings by satellite for image navigation

Stars are sensed by one or more instruments (1, 2) on board a three-axis stabilized satellite, for purposes of assisting in image navigation. A star acquistion computer (64), which may be located on the earth, commands the instrument mirror (33, 32) to slew just outside the limb of the earth or other celestial body around which the satellite is orbiting, to look for stars that have been cataloged in a star map stored within the computer (64). The instrument (1, 2) is commanded to dwell for a period of time equal to a star search window time, plus the maximum time the instrument (1, 2) takes to complete a current scan, plus the maximum time it takes for the mirror (33, 32) to slew to the star. When the satellite is first placed in orbit, and following first stationkeeping and eclipse, a special operation is performed in which the star-seeking instrument (1, 2) FOV is broadened. The elevation dimension can be broadened by performing repetitive star seeks; the azimuth dimension can be broadened by lengthening the commanded dwell times.

Kamel, Ahmed A.↗

NASA NTRS2016

Relative Terrain Imaging Navigation (RETINA) Tool for the Asteroid Redirect Robotic Mission (ARRM)

As a part of the NASA initiative to collect a boulder off of an asteroid and return it to Lunar orbit, the Satellite Servicing Capabilities Office (SSCO) and NASA GSFC are developing an on-board relative terrain imaging navigation algorithm for the Asteroid Redirect Robotic Mission (ARRM). After performing several flybys and dry runs to verify and refine the shape, spin, and gravity models and obtain centimeter level imagery, the spacecraft will descend to the surface of the asteroid to capture a boulder and return it to Lunar Orbit. The algorithm implements Stereophotoclinometry methods to register landmarks with images taken onboard the spacecraft, and use these measurements to estimate the position and orientation of the spacecraft with respect to the asteroid. This paper will present an overview of the ARRM GN&C system and concept of operations as well as a description of the algorithm and its implementation. These techniques will be demonstrated for the descent to the surface of the proposed asteroid of interest, 2008 EV5, and preliminary results will be shown.

Navigation↗

NASA NTRS2019

GOES I-M: A Retrospective Look at Image Navigation and Registration (INR), Jitter and Lessons Learned

The Geostationary Operational Environmental Satellite (GOES) I-M series of spacecraft was the second generation of United States meteorological observational platforms in geosynchronous orbit. They served as the principal Earth- viewing observational platforms for continuously monitoring dynamic weather events from the mid-1990s and into the 21st century. This paper will look back at the program framing key system attributes of the mission, which necessitated a multi-layered development approach to meet stringent meteorological instrument Line-of-Sight (LoS) pointing and pointing stability requirements. The overall approach involved understanding, correcting, and avoiding pointing errors across a broad frequency range including what would typically be called dynamic interaction and jitter. Background information will be provided covering the mission architecture and program drivers. The systems solution for man- aging and mitigating the deleterious influences of on-board disturbances in order to meet the challenging instrument LoS pointing and jitter requirements will be described, along with the ‘first of its kind’ Image Navigation and Registration system. A broad look back at the lessons learned that emerged from the GOES I- M experience will be presented, with the intent of capturing general and specific insights for developers of future missions having stringent payload instrument pointing requirements. These discussions will touch on such critical aspects as defining jitter and related pointing requirements, the importance of early system architectural decisions, understanding and reducing on-board disturbances, the balance of test and analysis, and the imperative for maximizing on-orbit operational flexibility in order to accommodate unexpected dynamic interactions.

Sudey, John↗

NASA NTRS1987

Satellite camera image navigation

Pixels within a satellite camera (1, 2) image are precisely located in terms of latitude and longitude on a celestial body, such as the earth, being imaged. A computer (60) on the earth generates models (40, 50) of the satellite's orbit and attitude, respectively. The orbit model (40) is generated from measurements of stars and landmarks taken by the camera (1, 2), and by range data. The orbit model (40) is an expression of the satellite's latitude and longitude at the subsatellite point, and of the altitude of the satellite, as a function of time, using as coefficients (K) the six Keplerian elements at epoch. The attitude model (50) is based upon star measurements taken by each camera (1, 2). The attitude model (50) is a set of expressions for the deviations in a set of mutually orthogonal reference optical axes (x, y, z) as a function of time, for each camera (1, 2). Measured data is fit into the models (40, 50) using a walking least squares fit algorithm. A transformation computer (66 ) transforms pixel coordinates as telemetered by the camera (1, 2) into earth latitude and longitude coordinates, using the orbit and attitude models (40, 50).

Kamel, Ahmed A.↗