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NASA NTRS1987

Acquisition, tracking, and pointing; Proceedings of the Meeting, Orlando, FL, Apr. 3, 4, 1986

The present conference discusses the effect of target signatures on active tracking, the high resolution obtainable with a two-degrees-of-freedom angle sensor having a high update rate, solar object tracking for the Hubble Space Telescope, scaled experiments for the assessment of precise active tracking, and large aperture high-accuracy satellite laser tracking. Also discussed are laboratory test results for the Spaceborne Geodynamic Ranging System's high speed optical tracking system, jitter stabilization for precise optical pointing, scan stabilization and jitter control for an airborne telescope, a simulation of the Solar Optical Telescope's pointing performance, a microprocessor-based dual-speed angle converter, and image localization for alignment by means of adaptive preprocessing.

Auelmann, Richard R.↗

NASA NTRSDate not supplied

Tracking the Hunga Tonga-Hunga Ha’apai Eruption Stratospheric Aerosol and Trace Gas Plumes Using Machine Learning

The Hunga Tonga-Hunga Ha’apai (HTHH) submarine volcano had an explosive eruption phase on January 15, 2022, that thrusted ash, gases, and water vapor through the troposphere and into the stratosphere. The stratospheric volcanic plume included aerosol precursor gases such as SO2 and H2S as well as anomalously high water vapor concentrations due to the submarine oceanic origin. With these atmospheric constituents, the sulfuric gases and water vapor formed sulfate (SO4) particles via gas-to-particle reactions and these aerosols likely increased in size due to hygroscopic growth within anomalously humid regions. Strong easterlies and gravity waves propagated the volcanic impacts throughout the stratosphere. Orbital and suborbital passive sensor retrievals detected changes in the aerosol and trace gas characteristics within the atmospheric column for cloud-free regions over the southern hemisphere. While the CALIPSO lidar can detect aerosol layers in the stratosphere, passive sensors such as MODIS retrieved the total column aerosol abundance and characteristics. Previous studies used manual tracking methods to determine volcanic plume positions and compared them to ground observations. In this study, we examine the machine learning (ML) approaches including segmentation, object detection, and object tracking to identify and track aerosol and trace gas plumes using orbital and suborbital sensor data. This ML implementation strives to provide a more systematic approach to separate total column effects from those of the stratosphere. Similar ML tracking may be useful for stratospheric impact events observed historically by CALIPSO and in the future with EarthCare and the Atmosphere Observing System (AOS) lidar-capable missions.

Rhys Leahy↗

NASA NTRS2003

EVA Robotic Assistant Project: Platform Attitude Prediction

The Robotic Systems Technology Branch is currently working on the development of an EVA Robotic Assistant under the sponsorship of the Surface Systems Thrust of the NASA Cross Enterprise Technology Development Program (CETDP). This will be a mobile robot that can follow a field geologist during planetary surface exploration, carry his tools and the samples that he collects, and provide video coverage of his activity. Prior experiments have shown that for such a robot to be useful it must be able to follow the geologist at walking speed over any terrain of interest. Geologically interesting terrain tends to be rough rather than smooth. The commercial mobile robot that was recently purchased as an initial testbed for the EVA Robotic Assistant Project, an ATRV Jr., is capable of faster than walking speed outside but it has no suspension. Its wheels with inflated rubber tires are attached to axles that are connected directly to the robot body. Any angular motion of the robot produced by driving over rough terrain will directly affect the pointing of the on-board stereo cameras. The resulting image motion is expected to make tracking of the geologist more difficult. This will either require the tracker to search a larger part of the image to find the target from frame to frame or to search mechanically in pan and tilt whenever the image motion is large enough to put the target outside the image in the next frame. This project consists of the design and implementation of a Kalman filter that combines the output of the angular rate sensors and linear accelerometers on the robot to estimate the motion of the robot base. The motion of the stereo camera pair mounted on the robot that results from this motion as the robot drives over rough terrain is then straightforward to compute. The estimates may then be used, for example, to command the robot s on-board pan-tilt unit to compensate for the camera motion induced by the base movement. This has been accomplished in two ways: first, a standalone head stabilizer has been implemented and second, the estimates have been used to influence the search algorithm of the stereo tracking algorithm. Studies of the image motion of a tracked object indicate that the image motion of objects is suppressed while the robot crossing rough terrain. This work expands the range of speed and surface roughness over which the robot should be able to track and follow a field geologist and accept arm gesture commands from the geologist.

Nickels, Kevin M.↗

NASA NTRSDate not supplied

Automated Detection and Analysis of Resident Space Objects with the 1.3-Meter Eugene Stansbery-Meter Class Autonomous Telescope

Optical telescopes dedicated to the detection of orbital debris employ large-area detectors that generate a large number of images each night. Such surveys require automated data analysis pipelines that process the images and detect moving objects. We present an overview of the data analysis pipeline employed by the 1.3-meter Eugene Stansbery-Meter Class Autonomous Telescope (ES-MCAT) on Ascension Island, operated by NASA’s Orbital Debris Program Office. The pipeline enfolds the astrometric and photometric calibration of the images, star-trail removal, object detection, correlation over multiple sequential image frames, and orbital parameter estimation. The performance of the pipeline was investigated by means of Monte-Carlo simulations in which simulated object tracks were inserted into ES-MCAT images and then processed by the pipeline. This technique allows one to confidently estimate the completeness for the detection of resident space objects as a function of apparent magnitude and angular velocity. This paper discusses these techniques and provides examples using actual data.

Paul Hickson↗

NASA NTRS2023

Automated Detection and Analysis of Resident Space Objects with the 1.3-Meter Eugene Stansbery-Meter Class Autonomous Telescope

Paul Hickson↗

NASA NTRS1989

Real-time optical holographic tracking of multiple objects

A coherent optical correlation technique for real-time simultaneous tracking of several different objects making independent movements is described, and experimental results are presented. An evaluation of this system compared with digital computing systems is made. The real-time processing capability is obtained through the use of a liquid crystal television spatial light modulator and a dichromated gelatin multifocus hololens. A coded reference beam is utilized in the separation of the output correlation plane associated with each input target so that independent tracking can be achieved.

Chao, Tien-Hsin↗

NASA NTRS1993

Robust algebraic image enhancement for intelligent control systems

Robust vision capability for intelligent control systems has been an elusive goal in image processing. The computationally intensive techniques a necessary for conventional image processing make real-time applications, such as object tracking and collision avoidance difficult. In order to endow an intelligent control system with the needed vision robustness, an adequate image enhancement subsystem capable of compensating for the wide variety of real-world degradations, must exist between the image capturing and the object recognition subsystems. This enhancement stage must be adaptive and must operate with consistency in the presence of both statistical and shape-based noise. To deal with this problem, we have developed an innovative algebraic approach which provides a sound mathematical framework for image representation and manipulation. Our image model provides a natural platform from which to pursue dynamic scene analysis, and its incorporation into a vision system would serve as the front-end to an intelligent control system. We have developed a unique polynomial representation of gray level imagery and applied this representation to develop polynomial operators on complex gray level scenes. This approach is highly advantageous since polynomials can be manipulated very easily, and are readily understood, thus providing a very convenient environment for image processing. Our model presents a highly structured and compact algebraic representation of grey-level images which can be viewed as fuzzy sets.

Lerner, Bao-Ting↗

NASA NTRS2014

NASA's Newest Orbital Debris Ground-based Telescope Assets: MCAT and UKIRT

NASA's Orbital Debris Program Office (ODPO) will break ground on Ascension Island in 2014 to build the newest optical (0.30 - 1.06 micrometers) ground-based telescope asset dedicated to the study of orbital debris. The Meter Class Autonomous Telescope (MCAT) is a 1.3m optical telescope designed to track objects in orbits ranging from Low Earth Orbit (LEO) to Geosynchronous Earth Orbit (GEO). Ascension Island is located in the South Atlantic Ocean, offering longitudinal sky coverage not afforded by the Ground-based Electro-Optical Deep Space Surveillance (GEODSS) network. With a fast-tracking dome, a suite of visible wide-band filters, and a time-delay integration (TDI) capable camera, MCAT is capable of multiple observing modes ranging from tracking cataloged debris targets to surveying the overall debris environment. Access to the United Kingdom Infrared Telescope (UKIRT) will extend our spectral coverage into the near- (0.8-5 micrometers) and mid- to far-infrared (8-25 micrometers) regime. UKIRT is a 3.8m telescope located on Mauna Kea on the Big Island of Hawaii. At nearly 14,000-feet and above the atmospheric inversion layer, this is one of the premier astronomical sites in the world and is an ideal setting for an infrared telescope. An unprecedented one-third of this telescope's time has been allocated to collect orbital debris data for NASA's ODPO over a 2-year period. UKIRT has several instruments available to obtain low-resolution spectroscopy in both the near-IR and the mid/far-IR. Infrared spectroscopy is ideal for constraining the material types, albedos and sizes of debris targets, and potentially gaining insight into reddening effects caused by space weathering. In addition, UKIRT will be used to acquire broadband photometric imaging at GEO with the Wide Field Camera (WFCAM) for studying known objects of interest as well as collecting data in survey-mode to discover new targets. Results from the first stage of the debris campaign will be presented. The combination of these ground-based telescope assets will yield spectral coverage ranging from 0.3 - 25 micrmeters, allowing orbital debris to be studied in depth across a wider wavelength range in the visible and IR than ever previously studied by ODPO. Located on opposite sides of the world and in opposite hemispheres, they offer access to nearly the entire GEO belt on any given night, allowing immediate coverage of nearly any time-critical break-up event. By expanding the methods for surveying, detecting, and characterizing orbital debris, we can better model the debris environment and ultimately gain insight into how to mitigate potential collisions for future missions.

Lederer, S. M.↗

NASA NTRS1994

Investigation of water vapor motion winds from geostationary satellites

Water vapor imagery from geostationary satellites has been available for over a decade. These data are used extensively by operational analysts and forecasters, mainly in a qualitative mode (Weldon and Holmes 1991). In addition to qualitative applications, motions deduced in animated water vapor imagery can be used to infer wind fields in cloudless regimes, thereby augmenting the information provided by cloud-drift wind vectors. Early attempts at quantifying the data by tracking features in water vapor imagery met with modest success (Stewart et al. 1985; Hayden and Stewart 1987). More recently, automated techniques have been developed and refined, and have resulted in upper-level wind observations comparable in quality to current operational cloud-tracked winds (Laurent 1993). In a recent study by Velden et al. (1993) it was demonstrated that wind sets derived from Meteosat-3 (M-3) water vapor imagery can provide important environmental wind information in data void areas surrounding tropical cyclones, and can positively impact objective track forecasts. M-3 was repositioned to 75W by the European Space Agency in 1992 in order to provide complete coverage of the Atlantic Ocean. Data from this satellite are being transmitted to the U.S. for operational use. Compared with the current GOES-7 (G-7) satellite (positioned near 112W), the M-3 water vapor channel contains a superior horizontal resolution (5 km vs. 16 km ). In this paper, we examine wind sets derived using automated procedures from both GOES-7 and Meteosat-3 full disk water vapor imagery in order to assess this data as a potentially important source of large-scale wind information. As part of a product demonstration wind sets were produced twice a day at CIMSS during a six-week period in March and April (1994). These data sets are assessed in terms of geographic coverage, statistical accuracy, and meteorological impact through preliminary results of numerical model forecast studies.

Velden, Christopher S.↗

NASA NTRS2013

Characterization of the 2012-044c Briz-M Upper Stage Breakup

On 6 August, 2012, Russia launched two commercial satellites aboard a Proton rocket, and attempted to place them in geosynchronous orbit using a Briz-M upper stage (2012-044C, SSN 38746). Unfortunately, the upper stage failed early in its burn and was left stranded in an elliptical orbit with a perigee in low Earth orbit (LEO). Because the stage failed with much of its fuel on board, it was deemed a significant breakup risk. These fears were confirmed when it broke up 16 October, creating a large cloud of debris with perigees below that of the International Space Station. The debris cloud was tracked by the US Space Surveillance Network (SSN), which can reliably detect and track objects down to about 10 cm in size. Because of the unusual geometry of the breakup, there was an opportunity for NASA Orbital Debris Program Office to request radar assets to characterize the extent of the debris cloud in sizes smaller than the standard debris tracked by the SSN. This paper will describe the observation campaign to measure the small particle distributions of this cloud, and presents the results of the analysis of the data. We shall compare the data to the modelled size distribution, number, and shape of the cloud, and what implications this may have for future breakup debris models. We shall conclude the paper with a discussion how this measurement process can be improved for future breakups.

Matney, M. J.↗

NASA NTRS2013

Characterization of the 2012-044C Briz-M Upper Stage Breakup

On 6 August, 2012, Russia launched two commercial satellites aboard a Proton rocket, and attempted to place them in geosynchronous orbit using a Briz-M upper stage (2012-044C, SSN 38746). Unfortunately, the upper stage failed early in its burn and was left stranded in an elliptical orbit with a perigee in low Earth orbit (LEO). Because the stage failed with much of its fuel on board, it was deemed a significant breakup risk. These fears were confirmed when it broke up 16 October, creating a large cloud of debris with perigees below that of the International Space Station. The debris cloud was tracked by the US Space Surveillance Network (SSN), which can reliably detect and track objects down to about 10 cm in size. Because of the unusual geometry of the breakup, there was an opportunity for NASA Orbital Debris Program Office to use specialized radar assets to characterize the extent of the debris cloud in sizes smaller than the standard debris tracked by the SSN. This paper will describe the observation campaign to measure the small particle distributions of this cloud, and presents the results of the analysis of the data. We shall compare the data to the modelled size distribution, number, and shape of the cloud, and what implications this may have for future breakup debris models. We shall conclude the paper with a discussion how this measurement process can be improved for future breakups.

Matney, M. J.↗

NASA NTRS2004

Real-time Detection of Moving Objects from Moving Vehicles Using Dense Stereo and Optical Flow

Dynamic scene perception is very important for autonomous vehicles operating around other moving vehicles and humans. Most work on real-time object tracking from moving platforms has used sparse features or assumed flat scene structures. We have recently extended a real-time. dense stereo system to include realtime. dense optical flow, enabling more comprehensive dynamic scene analysis. We describe algorithms to robustly estimate 6-DOF robot egomotion in the presence of moving objects using dense flow and dense stereo. We then use dense stereo and egomotion estimates to identify other moving objects while the robot itself is moving. We present results showing accurate egomotion estimation and detection of moving people and vehicles under general 6DOF motion of the robot and independently moving objects. The system runs at 18.3 Hz on a 1.4 GHz Pentium M laptop. computing 160x120 disparity maps and optical flow fields, egomotion, and moving object segmentation. We believe this is a significant step toward general unconstrained dynamic scene analysis for mobile robots, as well as for improved position estimation where GPS is unavailable.

moving object detection↗

NASA NTRS2012

Lightning Jump Algorithm and Relation to Thunderstorm Cell Tracking, GLM Proxy and Other Meteorological Measurements

The lightning jump algorithm has a robust history in correlating upward trends in lightning to severe and hazardous weather occurrence. The algorithm uses the correlation between the physical principles that govern an updraft's ability to produce microphysical and kinematic conditions conducive for electrification and its role in the development of severe weather conditions. Recent work has demonstrated that the lightning jump algorithm concept holds significant promise in the operational realm, aiding in the identification of thunderstorms that have potential to produce severe or hazardous weather. However, a large amount of work still needs to be completed in spite of these positive results. The total lightning jump algorithm is not a stand-alone concept that can be used independent of other meteorological measurements, parameters, and techniques. For example, the algorithm is highly dependent upon thunderstorm tracking to build lightning histories on convective cells. Current tracking methods show that thunderstorm cell tracking is most reliable and cell histories are most accurate when radar information is incorporated with lightning data. In the absence of radar data, the cell tracking is a bit less reliable but the value added by the lightning information is much greater. For optimal application, the algorithm should be integrated with other measurements that assess storm scale properties (e.g., satellite, radar). Therefore, the recent focus of this research effort has been assessing the lightning jump's relation to thunderstorm tracking, meteorological parameters, and its potential uses in operational meteorology. Furthermore, the algorithm must be tailored for the optically-based GOES-R Geostationary Lightning Mapper (GLM), as what has been observed using Very High Frequency Lightning Mapping Array (VHF LMA) measurements will not exactly translate to what will be observed by GLM due to resolution and other instrument differences. Herein, we present some of the promising aspects and challenges encountered in utilizing objective tracking and GLM proxy data, as well as recent results that demonstrate the value added information gained by combining the lightning jump concept with traditional meteorological measurements.

Schultz, Christopher J.↗

NASA NTRS1994

Real-time tracking using stereo and motion: Visual perception for space robotics

The state-of-the-art in computing technology is rapidly attaining the performance necessary to implement many early vision algorithms at real-time rates. This new capability is helping to accelerate progress in vision research by improving our ability to evaluate the performance of algorithms in dynamic environments. In particular, we are becoming much more aware of the relative stability of various visual measurements in the presence of camera motion and system noise. This new processing speed is also allowing us to raise our sights toward accomplishing much higher-level processing tasks, such as figure-ground separation and active object tracking, in real-time. This paper describes a methodology for using early visual measurements to accomplish higher-level tasks; it then presents an overview of the high-speed accelerators developed at Teleos to support early visual measurements. The final section describes the successful deployment of a real-time vision system to provide visual perception for the Extravehicular Activity Helper/Retriever robotic system in tests aboard NASA's KC135 reduced gravity aircraft.

Nishihara, H. Keith↗

NASA NTRS1994

Dual use of image based tracking techniques: Laser eye surgery and low vision prosthesis

With a concentration on Fourier optics pattern recognition, we have developed several methods of tracking objects in dynamic imagery to automate certain space applications such as orbital rendezvous and spacecraft capture, or planetary landing. We are developing two of these techniques for Earth applications in real-time medical image processing. The first is warping of a video image, developed to evoke shift invariance to scale and rotation in correlation pattern recognition. The technology is being applied to compensation for certain field defects in low vision humans. The second is using the optical joint Fourier transform to track the translation of unmodeled scenes. Developed as an image fixation tool to assist in calculating shape from motion, it is being applied to tracking motions of the eyeball quickly enough to keep a laser photocoagulation spot fixed on the retina, thus avoiding collateral damage.

Juday, Richard D.↗

NASA NTRS1994

Dual Use of Image Based Tracking Techniques: Laser Eye Surgery and Low Vision Prosthesis

Juday, Richard D.↗

NASA NTRS2019

NASA’s Orbital Debris JAO/ES-MCAT Optical Telescope Facility on Ascension Island

The NASA Orbital Debris Program Office has a long-standing optical program begun over three and a half decades ago in 1984, designed to observe the Earth-orbiting environment with optical telescopes. Photometrically calibrated optical data provides a statistical sample for input to NASA models of the debris population for understanding the current and future debris environment around the Earth. Tracked objects and orbits allow for analysis of break-up events. Both known (correlated target in the SSN catalogue, or CT) and unknown (uncorrelated target, or UCT) objects are of interest to better understand how to protect current spacecraft and design more robust future operational satellites, and advise on how policies and practices can lead to protecting the environment itself for future generations. In 2015, a joint NASA JSC – Air Force Research Labs (AFRL) project culminated in the installation of the 1.3-meter Eugene Stansbery Meter Class Autonomous Telescope, ES-MCAT (a.k.a. MCAT) on Ascension Island. This DFM Engineering designed telescope provides nearly five-times greater light-collecting power than its predecessor, the 0.6-m MODEST telescope, and faster tracking capabilities by both the telescope and the 7-m ObservaDome. This allows for all orbital regimes to be easily within reach, ranging from low Earth to geosynchronous orbits. Extensive testing and commissioning activities of this custom system led to successfully reaching Initial Operational Capability in 2018, and the facility is currently on track to reach Full Operational Capability. The John Africano Observatory (JAO) comprises the primary 1.3-m ES-MCAT facility, the adjacent tower platform with a 0.4-m telescope, a sophisticated suite of weather instruments, and custom software by Euclid Research for autonomously running the entire system, including monitoring the weather and hardware, tasking all components, and collecting, processing, and analyzing the data. The mission of JAO and MCAT will be discussed, including survey and tracking tasking, a full discussion of data calibration, and both optics and weather-dependent performance.

Lederer, S. M.↗

NASA NTRS1993

Multiple-camera/motion stereoscopy for range estimation in helicopter flight

Aiding the pilot to improve safety and reduce pilot workload by detecting obstacles and planning obstacle-free flight paths during low-altitude helicopter flight is desirable. Computer vision techniques provide an attractive method of obstacle detection and range estimation for objects within a large field of view ahead of the helicopter. Previous research has had considerable success by using an image sequence from a single moving camera to solving this problem. The major limitations of single camera approaches are that no range information can be obtained near the instantaneous direction of motion or in the absence of motion. These limitations can be overcome through the use of multiple cameras. This paper presents a hybrid motion/stereo algorithm which allows range refinement through recursive range estimation while avoiding loss of range information in the direction of travel. A feature-based approach is used to track objects between image frames. An extended Kalman filter combines knowledge of the camera motion and measurements of a feature's image location to recursively estimate the feature's range and to predict its location in future images. Performance of the algorithm will be illustrated using an image sequence, motion information, and independent range measurements from a low-altitude helicopter flight experiment.

Smith, Phillip N.↗