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Jet Propulsion Laboratory's (JPL) Near-Earth Asteroid Tracking (NEAT) program has two simultaneously-operating, autonomous search systems on two geographically-separated 1.2-m telescopes; one at the Maui Space Surveillance System (NEAT/MSSS) and the other on the Palomar Observatory's Oschin telescope (NEAT/Palomar). This paper will focus exclusively on the NEAT/MSSS system.
Meteorological and environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. These parameters can most conveniently be obtained using remote sensing. Selected provinces and districts in Thailand and Indonesia are used to illustrate how remotely sensed meteorological and environmental parameters may enhance the capabilities for malaria surveillance and control. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records.
Over the last year the Jet Propulsion Laboratory's (JPL) Near-Earth Asteroid Tracking (NEAT) program has made significant progress and now consists of two simultaneously-operating, autonomous search systems on the 1.2-m (48") telescopes: on the Maui Space Surveillance System (NEAT/MSSS) and NEAT/Palomar on the Palomar Observatory's Oschin telescope. This paper will focus exclusively on the NEAT/MSSS system. NEAT/MSSS is operated as a partnership between NASA/JPL and the United States Air Force Research Laboratory (AFRL), utilizing the AFRL 1.2-m telescope on the 3000-m summit of Haleakala, Maui, The USAF Space Command (SPCMD) contributed financial support to build and install the 'NEAT focal reducer' on the MSSS 1.2-m telescope giving it a large field of view (2.5 square degrees), suitable for the near-earth object (NEO),both asteroids and comets, survey. This work was completed in February 2000. AFRL has made a commitment to NEAT/MSSS that allows NEAT to operate full time with the understanding that AFRL participate as partners in NEAT/MSSS and have use of the NEAT camera system for high priority satellite observations during bright time (parts of 12 nights each month). Currently, NEAT has discovered 42 NEAs including 12 larger than 1-km, 5 Potentially Hazardous Asteroids (PHAs), 6 comets, and nearly 25,000 asteroid detections since March 2000.
A system and method which partitions a parameter estimation model, a fault detection model, and a fault classification model for a process surveillance scheme into two or more coordinated submodels together providing improved diagnostic decision making for at least one determined operating mode of an asset.
A system and method for monitoring an apparatus or process asset including creating a process model comprised of a plurality of process submodels each correlative to at least one training data subset partitioned from an unpartitioned training data set and each having an operating mode associated thereto; acquiring a set of observed signal data values from the asset; determining an operating mode of the asset for the set of observed signal data values; selecting a process submodel from the process model as a function of the determined operating mode of the asset; calculating a set of estimated signal data values from the selected process submodel for the determined operating mode; and determining asset status as a function of the calculated set of estimated signal data values for providing asset surveillance and/or control.
A system and method for monitoring an apparatus or process asset including partitioning an unpartitioned training data set into a plurality of training data subsets each having an operating mode associated thereto; creating a process model comprised of a plurality of process submodels each trained as a function of at least one of the training data subsets; acquiring a current set of observed signal data values from the asset; determining an operating mode of the asset for the current set of observed signal data values; selecting a process submodel from the process model as a function of the determined operating mode of the asset; calculating a current set of estimated signal data values from the selected process submodel for the determined operating mode; and outputting the calculated current set of estimated signal data values for providing asset surveillance and/or control.
System and method for providing surveillance of an asset comprised of numerically fitting at least one mathematical model to obtained residual data correlative to asset operation; storing at least one mathematical model in a memory; obtaining a current set of signal data from the asset; retrieving at least one mathematical model from the memory, using the retrieved mathematical model in a sequential hypothesis test for determining if the current set of signal data is indicative of a fault condition; determining an asset fault cause correlative to a determined indication of a fault condition; providing an indication correlative to a determined fault cause, and an action when warranted. The residual data can be mode partitioned, a current mode of operation can be determined from the asset, and at least one mathematical model can be retrieved from the memory as a function of the determined mode of operation.
The U.S. Space Surveillance Network (SSN) issues formal satellite reentry predictions for objects which have the potential for generating debris which could pose a hazard to people or property on Earth. These prognostications, known as Tracking and Impact Prediction (TIP) messages, are nominally distributed at daily intervals beginning four days prior to the anticipated reentry and several times during the final 24 hours in orbit. The accuracy of these messages depends on the nature of the satellite s orbit, the characteristics of the space vehicle, solar activity, and many other factors. Despite the many influences on the time and the location of reentry, a useful assessment of the accuracies of TIP messages can be derived and compared with the official accuracies included with each TIP message. This paper summarizes the results of a study of numerous uncontrolled reentries of spacecraft and rocket bodies from nearly circular orbits over a span of several years. Insights are provided into the empirical accuracies and utility of SSN TIP messages.
On January 11, 2007, the Chinese military conducted a test of an anti-satellite (ASAT) system, destroying their own Fengyun-1C spacecraft with an interceptor missile. The resulting hypervelocity collision created an unprecedented number of tracked debris - more than 2500 objects. These objects represent only those large enough for the US Space Surveillance Network (SSN) to track - typically objects larger than about 5-10 cm in diameter. There are expected to be even more debris objects at sizes too small to be seen and tracked by the SSN. Because of the altitude of the target satellite (865 x 845 km orbit), many of the debris are expected to have long orbital lifetimes and contribute to the orbital debris environment for decades to come. In the days and weeks following the ASAT test, NASA was able to use Lincoln Laboratory s Haystack radar on several occasions to observe portions of the ASAT debris cloud. Haystack has the capability of detecting objects down to less than one centimeter in diameter, and a large number of centimeter-sized particles corresponding to the ASAT cloud were clearly seen in the data. While Haystack cannot track these objects, the statistical sampling procedures NASA uses can give an accurate statistical picture of the characteristics of the debris from a breakup event. For years computer models based on data from ground hypervelocity collision tests (e.g., the SOCIT test) and orbital collision experiments (e.g., the P-78 and Delta-180 on-orbit collisions) have been used to predict the extent and characteristics of such hypervelocity collision debris clouds, but until now there have not been good ways to verify these models in the centimeter size regime. It is believed that unplanned collisions of objects in space similar to ASAT tests will drive the long-term future evolution of the debris environment in near-Earth space. Therefore, the Chinese ASAT test provides an excellent opportunity to test the models used to predict the future debris environment. For this study, Haystack detection events are compared to model predictions to test the model assumptions, including debris size distribution, velocity distribution, and assumptions about momentum transfer between the target and interceptor. In this paper we will present the results of these and other measurements on the size and extent of collisional breakup debris clouds.
Background: The transmission of vectorborne infectious diseases is often influenced by environmental, meteorological and climatic parameters, because the vector life cycle depends on these factors. For example, the geophysical parameters relevant to malaria transmission include precipitation, surface temperature, humidity, elevation, and vegetation type. Because these parameters are routinely measured by satellites, remote sensing is an important technological tool for predicting, preventing, and containing a number of vectorborne infectious diseases, such as malaria, dengue, West Nile virus, etc. Methods: A variety of NASA remote sensing data can be used for modeling vectorborne infectious disease transmission. We will discuss both the well known and less known remote sensing data, including Landsat, AVHRR (Advanced Very High Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer), TRMM (Tropical Rainfall Measuring Mission), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), EO-1 (Earth Observing One) ALI (Advanced Land Imager), and SIESIP (Seasonal to Interannual Earth Science Information Partner) dataset. Giovanni is a Web-based application developed by the NASA Goddard Earth Sciences Data and Information Services Center. It provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data. After remote sensing data is obtained, a variety of techniques, including generalized linear models and artificial intelligence oriented methods, t 3 can be used to model the dependency of disease transmission on these parameters. Results: The processes of accessing, visualizing and utilizing precipitation data using Giovanni, and acquiring other data at additional websites are illustrated. Malaria incidence time series for some parts of Thailand and Indonesia are used to demonstrate that malaria incidences are reasonably well modeled with generalized linear models and artificial intelligence based techniques. Conclusions: Remote sensing data relevant to the transmission of vectorborne infectious diseases can be conveniently accessed at NASA and some other websites. These data are useful for vectorborne infectious disease surveillance and modeling.
As part of the National Environmental Public Health Tracking Network (EPHTN) the National Center for Environmental Health (NCEH) at the Centers for Disease Control and Prevention (CDC) is leading a project called Health and Environment Linked for Information Exchange (HELIX-Atlanta). The goal of developing the National Environmental Public Health Tracking Network is to improve the health of communities. Currently, few systems exist at the state or national level to concurrently track many of the exposures and health effects that might be associated with environmental hazards. An additional challenge is estimating exposure to environmental hazards such as particulate matter whose aerodynamic diameter is less than or equal to 2.5 micrometers (PM(2.5)) HELIX-Atlanta's goal is to examine the feasibility of building an integrated electronic health and environmental data network in five counties of Metropolitan Atlanta, GA (Clayton, Cobb, DeKalb, Fulton, and Gwinnett counties). Under HELIX-Atlanta, pilot projects are being conducted to develop methods to characterize exposure; link health and environmental data; analyze the relationship between health and environmental factors; and communicate findings. NASA Marshall Space Flight Center (NASA/MSFC) is collaborating with CDC to combine NASA earth science satellite observations related to air quality and environmental monitoring data to model surface estimates of PM(2.5) concentrations that can be linked with clinic visits for asthma. From 1999-2000 there were over 9,400 hospitalizations per year in Georgia with asthma as the primary diagnosis. The majority of these hospitalizations occurred in medical facilities in the five most populous Metro-Atlanta counties. Hospital charges resulting from asthma in Georgia are approximately $59 million dollars annually. There is evidence in the research literature that asthmatic persons are at increased risk of developing asthma exacerbations with exposure to environmental factors, including PM(2.5). Thus, HELIX-Atlanta is focusing on methods for characterizing population exposure to PM(2.5) for the Atlanta metropolitan area that could be used in on-going surveillance. While use of the Air Quality System, (AQS) PM(2.5) data alone could meet HELIX Atlanta, specifications, there are only five AQS sites in the Atlanta area, thus the spatial coverage is not ideal. Also, the AQS ground observations are made at time intervals ranging from one hour to six days leaving some temporal gaps. NASA Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Aerosol Optical Depth (AOD) data have the potential for estimating daily ground level PM(2.5) at 10 km resolution over the metropolitan Atlanta area supplementing the AQS ground observations and filling their spatial and temporal gaps.
New generations of surveillance drones are being outfitted with numerous high definition cameras. The rapid proliferation of fielded sensors and supporting capacity for processing and displaying data will translate into ever more capable platforms, but with increased capability comes increased complexity and scale that may diminish the usefulness of such platforms to human operators. We investigate methods for alleviating strain on analysts by automatically retrieving content specific to their current task using a machine learning technique known as Multi-Instance Learning (MIL). We use MIL to create a real time model of the analysts' task and subsequently use the model to dynamically retrieve relevant content. This paper presents results from a pilot experiment in which a computer agent is assigned analyst tasks such as identifying caravanning vehicles in a simulated vehicle traffic environment. We compare agent performance between MIL aided trials and unaided trials.
Miniature blimps are under development as robots for use in exploring the thick, cold, nitrogen atmosphere of Saturn's moon, Titan. Similar blimps can also be used for surveillance and collection of biochemical samples in buildings, caves, subways, and other, similar structures on Earth. The widely perceived need for means to thwart attacks on buildings and to mitigate the effects of such attacks has prompted consideration of the use of robots. Relative to rover-type (wheeled) robots that have been considered for such uses, miniature blimps offer the advantage of ability to move through the air in any direction and, hence, to perform tasks that are difficult or impossible for wheeled robots, including climbing stairs and looking through windows. In addition, miniature blimps are expected to have greater range and to cost less, relative to wheeled robots.
Topics covered include: Develop Program/Project Quality Assurance Surveillance Plans The work activities performed by the developer and/or his suppliers are subject to evaluation and audit by government-designated representatives. CSO supports project by selecting on-site supplier representative s by one of several methods: (1) a Defense Contract Management Agency (DCMA) person via a Letter Of Delegation (LOD), (2) an independent assurance contractor (IAC) via a contract Audits, Assessments, and Assurance (A3) Contract Code 300 Mission Assurance Support Contract (MASC)
The 45th Weather Squadron (45 WS) is replacing the Weather Surveillance Radar, Model 74C (WSR-74C) at Patrick Air Force Base (PAFB), with a Doppler, dual polarization radar, the Radtec 43/250. A new scan strategy is needed for the Radtec 43/250, to provide high vertical resolution data over the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) launch pads, while taking advantage of the new radar's advanced capabilities for detecting severe weather phenomena associated with convection within the 45 WS area of responsibility. The Applied Meteorology Unit (AMU) developed several scan strategies customized for the operational needs of the 45 WS. The AMU also developed a plan for evaluating the scan strategies in the period prior to operational acceptance, currently scheduled for November 2008.
The 45th Weather Squadron (45 WS) forecasters at Cape Canaveral Air Force Station (CCAFS) in Florida include a probability of thunderstorm occurrence in their daily morning briefings. This information is used by personnel involved in determining the possibility of violating Launch Commit Criteria, evaluating Flight Rules for the Space Shuttle, and daily planning for ground operation activities on Kennedy Space Center (KSC)/CCAFS. Much of the current lightning probability forecast is based on a subjective analysis of model and observational data. The forecasters requested that a lightning probability forecast tool based on statistical analysis of historical warm-season (May - September) data be developed in order to increase the objectivity of the daily thunderstorm probability forecast. The tool is a set of statistical lightning forecast equations that provide a lightning occurrence probability for the day by 1100 UTC (0700 EDT) during the warm season. This study used 15 years (1989-2003) of warm season data to develop the objective forecast equations. The local CCAFS 1000 UTC sounding was used to calculate stability parameters for equation predictors. The Cloud-to-Ground Lightning Surveillance System (CGLSS) data were used to determine lightning occurrence for each day. The CGLSS data have been found to be more reliable indicators of lightning in the area than surface observations through local informal analyses. This work was based on the results from two earlier research projects. Everitt (1999) used surface observations and rawinsonde data to develop logistic regression equations that forecast the daily thunderstorm probability at CCAFS. The Everitt (1999) equations showed an improvement in skill over the Neumann-Pfeffer thunderstorm index (Neumann 1971), which uses multiple linear regression, and also persistence and climatology forecasts. Lericos et al. (2002) developed lightning distributions over the Florida peninsula based on specific flow regimes. The flow regimes were inferred from the average wind direction in the 1000-700 mb layer at Miami (MIA), Tampa (TBW), and Jacksonville (JAX), Florida, and the lightning data were from the National Lightning Detection Network. The results suggested that the daily flow regime may be an important predictor of lightning occurrence on KSC/CCAFS.
The NASA Kennedy Space Center (KSC) and Air Force Eastern Range (ER) are located in a region of Florida that experiences the highest area density of lightning strikes to ground in the United States, with values approaching 16 fl/km 2/yr when accumulated in 10x10 km (100 sq km) grids (see Figure 1). Consequently, the KSC-ER use data derived from two cloud-to-ground (CG) lightning detection networks to detect hazardous weather, the "Cloud-to-Ground Lightning Surveillance System" (CGLSS) that is owned and operated by the Air Force and the U.S. National Lightning Detection Network (NLDN) that is owned and operated by Vaisala, Inc. These systems are used to provide lightning warnings for ground operations and to insure mission safety during space launches at the KSC-ER. In order to protect the rocket and shuttle fleets, NASA and the Air Force follow a set of lightning safety guidelines that are called the Lightning Launch Commit Criteria (LLCC). These rules are designed to insure that vehicles are not exposed to the hazards of natural or triggered lightning that would in any way jeopardize a mission or cause harm to the shuttle astronauts. Also, if any CG lightning strikes too close to a vehicle on a launch pad, it can cause time-consuming mission delays due to the extensive retests that are often required for vehicles and/or payloads when this occurs. If any CG lightning strike is missed or mis-located by even a small amount, the result could have significant safety implications, require expensive retests, or create unnecessary delays or scrubs in launches. Therefore, it is important to understand the performance of each lightning detection system in considerable detail.
Astronauts perform physically demanding tasks and risk incurring musculoskeletal injuries during both groundbased training and missions. Increased injury rates throughout the history of the U.S. space program have been attributed to numerous factors, including an aging astronaut corps, increased Weightless Environment Training Facility (WETF) and Neutral Buoyancy Laboratory (NBL) training to construct the International Space Station, and improved clinical operations that promote injury prevention and reporting. With NASA program changes through the years (including retirement of the Shuttle program) and an improved training environment (including a new astronaut gym), there is no surveillance program to systematically track injury rates. A limited number of research projects have been conducted over the past 20 years to evaluate musculoskeletal injuries: (1) to evaluate orthopedic injuries from 1987 to 1995, (2) to describe upper extremity injuries, (3) to evaluate EVA spacesuit training related injuries, and (4) to evaluate in-flight musculoskeletal injuries. Nevertheless, there has been no consistently performed comprehensive assessment of musculoskeletal injuries among astronauts. The Barell Injury Diagnosis Matrix was introduced at the 2001 meeting of the International Collaborative Effort (ICE) on Injury Statistics. The Matrix proposes a standardized method of classifying body region by nature of injury. Diagnoses are coded using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coding system. The purpose of this study is to assess the usefulness and complexity of the Barell Injury Diagnosis Matrix to classify and track musculoskeletal injuries among NASA astronauts.