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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Increasing Cognitive Ability/Reserve Using Software – Pilot (ICARUS-Pilot)

BACKGROUND This research study was competitively awarded under the 2022 JSC Innovation Charge Account (ICA) program administered by NASA Johnson Space Center’s Joint Technology Working Group. Study period of performance was May through September 2022, with a maximum allowed procurement budget of $10K. The study sought to quantify and assess the potential benefit of using commercial-off-the-shelf (COTS) cognitive training software to improve cognitive performance in an astronaut-like terrestrial population. METHODS Five volunteer research participants were recruited from the JSC employee population to mimic certain demographic characteristics of the NASA astronaut population (age, education/discipline). Participant cognitive performance was assessed before and after executing eighteen sessions of remote cognitive training executed nominally three times per week using six exercises within an adaptive app-based COTS software package (BrainHQ, Posit Science) on study-provided tablets. Pre- and post-training cognitive performance was measured using internal assessments in BrainHQ as well as Cognition Test Battery (CTB) version ISS B01 v3 (3.0.9-201710021500), an independent software test developed specifically for NASA and used currently in research studies on astronauts. BrainHQ exercises were posited to map well or partially to several CTB sub-tests. Participants provided feedback on their study experience formally via semi-structured interview at the conclusion of testing and informally throughout the study if they encountered issues. RESULTS The enrolled ICARUS-Pilot study participants generally matched Artemis crew demographic characteristics. Four of five participants have completed study training and assessment activities as of the writing of this abstract. These test participants complied well with desired training session frequency and duration yielding an average cumulative active training duration of 15 hours over an average of 45 days; participants showed 78% average improvement in metric performance for the six trained exercises, with an associated overall 33%ile ranking increase against performance of the entire BrainHQ subscribing population for internal pre/post assessment, agreeing with post-study survey self-reported performance increases. CTB overall feedback scoring, not corrected for learning effects, showed an average of 19% performance improvement across its 10 performance measures over the training period for the completed participants. Detailed analyses will be conducted once participant data collection for the study is complete and the resulting dataset is fully populated. DISCUSSION These preliminary results provide a positive trend for the effectiveness of the training approach, but further analysis will be needed to establish significance, investigate far transfer, and suggest the needed participant pool size for subsequent efforts to achieve statistically significant outcomes given similar results. The pilot study has already been helpful by allowing the study team to learn a great deal about the capabilities and limitations of the COTS software package that will be reflected in future proposals along with revised timelines for study execution and test participant management. From participant feedback, one common thread regarding the COTS training was that it felt overly repetitive – future proposals should reassess overall training duration, available levels for each trained exercise, and the behavior of the BrainHQ internal scheduler in determining which exercises should be trained and for how long. If the final analysis of this feasibility study ultimately supports it, the study team will recommend further investigation to fully evaluate this potential countermeasure and optimize its implementation. Future proposals would cite this feasibility study’s outcome and would seek to refine the training protocol and obtain statistically significant results for cognitive performance increases as well as retention data.

cognitive training↗
NASA NTRSDate not supplied

Increasing Cognitive Ability/Reserve Using Software – Pilot (ICARUS-Pilot)

Background: This research study was competitively awarded under the 2022 JSC Innovation Charge Account (ICA) program administered by NASA Johnson Space Center’s Joint Technology Working Group. Study period of performance was May through September 2022, with a maximum allowed procurement budget of $10K. The study sought to quantify and assess the potential benefit of using commercial-off-the-shelf (COTS) cognitive training software to improve cognitive performance in an astronaut-like terrestrial population. Methods: Five volunteer research participants were recruited from the JSC employee population to mimic certain demographic characteristics of the NASA astronaut population (age, education/discipline). Participant cognitive performance was assessed before and after executing eighteen sessions of remote cognitive training executed nominally three times per week using six exercises within an adaptive app-based COTS software package (BrainHQ, Posit Science) on study-provided tablets. Pre- and post-training cognitive performance was measured using internal assessments in BrainHQ as well as Cognition Test Battery (CTB) version ISS B01 v3 (3.0.9-201710021500), an independent software test developed specifically for NASA and used currently in research studies on astronauts. BrainHQ exercises were posited to map well or partially to several CTB sub-tests. Participants provided feedback on their study experience formally via semi-structured interview at the conclusion of testing and informally throughout the study if they encountered issues. Results: The enrolled ICARUS-Pilot study participants generally matched Artemis crew demographic characteristics. Four of five participants have completed study training and assessment activities as of the writing of this abstract. These test participants complied well with desired training session frequency and duration yielding an average cumulative active training duration of 15 hours over an average of 45 days; participants showed 78% average improvement in metric performance for the six trained exercises, with an associated overall 33%ile ranking increase against performance of the entire BrainHQ subscribing population for internal pre/post assessment, agreeing with post-study survey self-reported performance increases. CTB overall feedback scoring, not corrected for learning effects, showed an average of 19% performance improvement across its 10 performance measures over the training period for the completed participants. Detailed analyses will be conducted once participant data collection for the study is complete and the resulting dataset is fully populated. Discussion: These preliminary results provide a positive trend for the effectiveness of the training approach, but further analysis will be needed to establish significance, investigate far transfer, and suggest the needed participant pool size for subsequent efforts to achieve statistically significant outcomes given similar results. The pilot study has already been helpful by allowing the study team to learn a great deal about the capabilities and limitations of the COTS software package that will be reflected in future proposals along with revised timelines for study execution and test participant management. From participant feedback, one common thread regarding the COTS training was that it felt overly repetitive – future proposals should reassess overall training duration, available levels for each trained exercise, and the behavior of the BrainHQ internal scheduler in determining which exercises should be trained and for how long. If the final analysis of this feasibility study ultimately supports it, the study team will recommend further investigation to fully evaluate this potential countermeasure and optimize its implementation. Future proposals would cite this feasibility study’s outcome and would seek to refine the training protocol and obtain statistically significant results for cognitive performance increases as well as retention data.

cognitive training↗
NASA NTRS2005

Advanced Resistive Exercise Device (ARED) Flight Software (FSW): A Unique Approach to Exercise in Long Duration Habitats

ARED flight instrumentation software is associated with an overall custom designed resistive exercise system that will be deployed on the International Space Station (ISS). This innovative software application fuses together many diverse and new technologies into a robust and usable package. The software takes advantage of touchscreen user interface technology by providing a graphical user interface on a Windows based tablet PC, meeting a design constraint of keyboard-less interaction with flight crewmembers. The software interacts with modified commercial data acquisition (DAQ) hardware to acquire multiple channels of sensor measurment from the ARED device. This information is recorded on the tablet PC and made available, via International Space Station (ISS) Wireless LAN (WLAN) and telemetry subsystems, to ground based mission medics and trainers for analysis. The software includes a feature to accept electronically encoded prescriptions of exercises that guide crewmembers through a customized regimen of resistive weight training, based on personal analysis. These electronically encoded prescriptions are provided to the crew via ISS WLAN and telemetry subsystems. All personal data is securely associated with an individual crew member, based on a PIN ID mechanism.

Mangieri, Mark↗
NASA NTRS2006

Demonstration of a Balloon Borne Arc-Second Pointer Design

Many designs for utilizing stratospheric balloons as low-cost platforms on which to conduct space science experiments have been proposed throughout the years. A major hurdle in extending the range of experiments for which these vehicles are useful has been the imposition of the gondola dynamics on the accuracy with which an instrument can be kept pointed at a celestial target. A significant number of scientists have sought the ability to point their instruments with jitter in the arc-second range. This paper presents the design and analysis of a stratospheric balloon borne pointing system that is able to meet this requirement. The test results of a demonstration prototype of the design with similar ability are also presented. Discussion of a high fidelity controller simulation for design analysis is presented. The flexibility of the flight train is represented through generalized modal analysis. A multiple controller scheme is utilized for coarse and fine pointing. Coarse azimuth pointing is accomplished by an established pointing system, with extensive flight history, residing above the gondola structure. A pitch-yaw gimbal mount is used for fine pointing, providing orthogonal axes when nominally on target. Fine pointing actuation is from direct drive dc motors, eliminating backlash problems. An analysis of friction nonlinearities and a demonstration of the necessity in eliminating static friction are provided. A unique bearing hub design is introduced that eliminates static friction from the system dynamics. A control scheme involving linear accelerometers for enhanced disturbance rejection is also presented. Results from a linear analysis of the total system and the high fidelity simulation are given. Results from a generalized demonstration prototype are presented. Commercial off-the-shelf (COTS) hardware was used to demonstrate the efficacy and performance of the pointer design for a mock instrument. Sub-arcsecond pointing ability from a ground hang test setup is shown from the testing results. This paper establishes that the proposed control strategy can be made robustly stable with significant design margins. Also demonstrated is the efficacy of the proposed system in rejecting disturbances larger than those considered realistic. The system is implemented and demonstrates sub arc second pointing ability using COTS hardware. Finally, we see that sub arc-second pointing stability can be achieved for a large instrument pointing at an inertial target.

DeWeese, Keith D.↗
NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗
NASA NTRS1981

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

A set of training statistics for the 30 meter resolution simulated thematic mapper MSS data was generated based on land use/land cover classes. In addition to this supervised data set, a nonsupervised multicluster block of training statistics is being defined in order to compare the classification results and evaluate the effect of the different training selection methods on classification performance. Two test data sets, defined using a stratified sampling procedure incorporating a grid system with dimensions of 50 lines by 50 columns, and another set based on an analyst supervised set of test fields were used to evaluate the classifications of the TMS data. The supervised training data set generated training statistics, and a per point Gaussian maximum likelihood classification of the 1979 TMS data was obtained. The August 1980 MSS data was radiometrically adjusted. The SAR data was redigitized and the SAR imagery was qualitatively analyzed.

Hoffer, R. M.↗
NASA NTRS1995

Applied virtual reality in aerospace design

A virtual reality (VR) applications program has been under development at the Marshall Space Flight Center (MSFC) since 1989. The objectives of the MSFC VR Applications Program are to develop, assess, validate, and utilize VR in hardware development, operations development and support, mission operations training and science training. Before VR can be used with confidence in a particular application, VR must be validated for that class of applications. For that reason, specific validation studies for selected classes of applications have been proposed and are currently underway. These include macro-ergonomic 'control room class' design analysis, Spacelab stowage reconfiguration training, a full-body microgravity functional reach simulator, a gross anatomy teaching simulator, and micro-ergonomic design analysis. This paper describes the MSFC VR Applications Program and the validation studies.

Hale, Joseph P.↗
NASA NTRS1995

Applied Virtual Reality Research and Applications at NASA/Marshall Space Flight Center

A Virtual Reality (VR) applications program has been under development at NASA/Marshall Space Flight Center (MSFC) since 1989. The objectives of the MSFC VR Applications Program are to develop, assess, validate, and utilize VR in hardware development, operations development and support, mission operations training and science training. Before this technology can be utilized with confidence in these applications, it must be validated for each particular class of application. That is, the precision and reliability with which it maps onto real settings and scenarios, representative of a class, must be calculated and assessed. The approach of the MSFC VR Applications Program is to develop and validate appropriate virtual environments and associated object kinematic and behavior attributes for specific classes of applications. These application-specific environments and associated simulations will be validated, where possible, through empirical comparisons with existing, accepted tools and methodologies. These validated VR analytical tools will then be available for use in the design and development of space systems and operations and in training and mission support systems. Specific validation studies for selected classes of applications have been completed or are currently underway. These include macro-ergonomic "control-room class" design analysis, Spacelab stowage reconfiguration training, a full-body micro-gravity functional reach simulator, and a gross anatomy teaching simulator. This paper describes the MSFC VR Applications Program and the validation studies.

Hale, Joseph P.↗
NASA NTRS1990

Space Station Freedom crew training

The nature of the Space Station Freedom Program presents an array of new and enhanced challenges which need to be addressed en route to developing an effective and affordable infrastructure for crew training. Such an infrastructure is essential for the safety and success of the program. The three major challenges that affect crew training are the long lifetime of the program (thirty years), the interdependence of successive increments, and the participation of the three International Partners (Canada, European Space Agency, and Japan) and a myriad of experimenters. This paper addresses these major challenges as they drive the development of a crew training capability and the actual conduct of crew training.

Space Flight/education/organization & administrati↗
NASA NTRS2020

Analysis of Pilot Monitoring Skills and a Review of Training Effectiveness

The commercial aviation industry world-wide has identified a need for improved pilot monitoring and awareness (e.g., FAA, 2013, ICAO, 2016). More specifically, aviation safety data indicate that failures in pilots’ flight path management (FPM) monitoring and awareness have contributed to a range of undesired outcomes: accidents, major upsets, and non-compliance with air traffic control (ATC) guidance. The Federal Aviation Administration (FAA) has further stated that these types of FPM failures are likely to worsen with the increasingly complex air traffic control systems and FPM concepts proposed for NextGen (https://www.faa. gov/nextgen/what_is_nextgen/) operations (e.g., see Hah et al., 2017). Adding to this complexity is the introduction of increasingly automated aircraft systems that can increase monitoring burdens. One potential mitigation for this situation is to enhance pilot training for effective monitoring. NASA Ames Research Center was asked to identify and evaluate training approaches that have the potential to enhance pilots’ ability to effectively monitor for FPM (with the result of improved awareness). The focus of this work is to identify, develop or validate training guidance to improve pilot monitoring/awareness regarding FPM and mitigate the recent trend of accidents and incidents, especially loss of control (LOC) events. The result of this work should be input for improved industry standards and FAA guidance to reduce the risk of incidents and accidents due to inadequate pilot monitoring/awareness. This is the first of three reports that were developed for this project.

aviation human factors↗
NASA NTRS1982

LANDSAT technology transfer to the private and public sectors through community colleges and other locally available institutions, phase 2 program

A program established by NASA with the Environmental Research Institute of Michigan (ERIM) applies a network where the major participants are NASA, universities or research institutes, community colleges, and local private and public organizations. Local users are given an opportunity to obtain "hands on" training in LANDSAT data analysis and Geographic Information System (GIS) techniques using a desk top, interactive remote analysis station (RAS). The RAS communicates with a central computing facility via telephone line, and provides for generation of land use and land suitability maps and other data products via remote command. During the period from 22 September 1980 - 6 March 1982, 15 workshops and other training activities were successfully conducted throughout Michigan providing hands on training on the RAS terminals for 250 or more people and user awareness activities such as exhibits and demonstrations for 2,000 or more participants.

Rogers, R. H.↗
NASA NTRS2016

Simulation of Mission Phases

This position with the Simulation and Graphics Branch (ER7) at Johnson Space Center (JSC) provided an introduction to vehicle hardware, mission planning, and simulation design. ER7 supports engineering analysis and flight crew training by providing high-fidelity, real-time graphical simulations in the Systems Engineering Simulator (SES) lab. The primary project assigned by NASA mentor and SES lab manager, Meghan Daley, was to develop a graphical simulation of the rendezvous, proximity operations, and docking (RPOD) phases of flight. The simulation is to include a generic crew/cargo transportation vehicle and a target object in low-Earth orbit (LEO). Various capsule, winged, and lifting body vehicles as well as historical RPOD methods were evaluated during the project analysis phase. JSC core mission to support the International Space Station (ISS), Commercial Crew Program (CCP), and Human Space Flight (HSF) influenced the project specifications. The simulation is characterized as a 30 meter +V Bar and/or -R Bar approach to the target object's docking station. The ISS was selected as the target object and the international Low Impact Docking System (iLIDS) was selected as the docking mechanism. The location of the target object's docking station corresponds with the RPOD methods identified. The simulation design focuses on Guidance, Navigation, and Control (GNC) system architecture models with station keeping and telemetry data processing capabilities. The optical and inertial sensors, reaction control system thrusters, and the docking mechanism selected were based on CCP vehicle manufacturer's current and proposed technologies. A significant amount of independent study and tutorial completion was required for this project. Multiple primary source materials were accessed using the NASA Technical Report Server (NTRS) and reference textbooks were borrowed from the JSC Main Library and International Space Station Library. The Trick Simulation Environment and User Training Materials version 2013.0 release was used to complete the Trick tutorial. Multiple network privilege and repository permission requests were required in order to access previous simulation models. The project was also an introduction to computer programming and the Linux operating system. Basic C++ and Python syntax was used during the completion of the Trick tutorial. Trick's engineering analysis and Monte Carlo simulation capabilities were observed and basic space mission planning procedures were applied in the conceptual design phase. Multiple professional development opportunities were completed in addition to project duties during this internship through the System for Administration, Training, and Education Resources for NASA (SATERN). Topics include: JSC Risk Management Workshop, CCP Risk Management, Basic Radiation Safety Training, X-Ray Radiation Safety, Basic Laser Safety, JSC Export Control, ISS RISE Ambassador, Basic SharePoint 2013, Space Nutrition and Biochemistry, and JSC Personal Protective Equipment. Additionally, this internship afforded the opportunity for formal project presentation and public speaking practice. This was my first experience at a NASA center. After completing this internship I have a much clearer understanding of certain aspects of the agency's processes and procedures, as well as a deeper appreciation from spaceflight simulation design and testing. I will continue to improve my technical skills so that I may have another opportunity to return to NASA and Johnson Space Center.

Carlstrom, Nicholas Mercury↗
NASA NTRS2007

Orion Entry, Descent, and Landing Simulation

The Orion Entry, Descent, and Landing simulation was created over the past two years to serve as the primary Crew Exploration Vehicle guidance, navigation, and control (GN&C) design and analysis tool at the National Aeronautics and Space Administration (NASA). The Advanced NASA Technology Architecture for Exploration Studies (ANTARES) simulation is a six degree-of-freedom tool with a unique design architecture which has a high level of flexibility. This paper describes the decision history and motivations that guided the creation of this simulation tool. The capabilities of the models within ANTARES are presented in detail. Special attention is given to features of the highly flexible GN&C architecture and the details of the implemented GN&C algorithms. ANTARES provides a foundation simulation for the Orion Project that has already been successfully used for requirements analysis, system definition analysis, and preliminary GN&C design analysis. ANTARES will find useful application in engineering analysis, mission operations, crew training, avionics-in-the-loop testing, etc. This paper focuses on the entry simulation aspect of ANTARES, which is part of a bigger simulation package supporting the entire mission profile of the Orion vehicle. The unique aspects of entry GN&C design are covered, including how the simulation is being used for Monte Carlo dispersion analysis and for support of linear stability analysis. Sample simulation output from ANTARES is presented in an appendix.

Hoelscher, Brian R.↗
NASA NTRS2019

Application of High-Dimensional Fuzzy K-Means Cluster Analysis to CALIOP/CALIPSO Version 4.1 Cloud-Aerosol Discrimination

This study applies fuzzy k-means (FKM) cluster analyses to a subset of the parameters reported in the CALIPSO lidar level 2 data products in order to classify the layers detected as either clouds or aerosols. The results obtained are used to assess the reliability of the cloud–aerosol discrimination (CAD) scores reported in the version 4.1 release of the CALIPSO data products. FKM is an unsupervised learning algorithm, whereas the CALIPSO operational CAD algorithm (COCA) takes a highly supervised approach. Despite these substantial computational and architectural differences, our statistical analyses show that the FKM classifications agree with the COCA classifications for more than 94 % of the cases in the troposphere. This high degree of similarity is achieved because the lidar-measured signatures of the majority of the clouds and the aerosols are naturally distinct, and hence objective methods can independently and effectively separate the two classes in most cases. Classification differences most often occur in complex scenes (e.g., evaporating water cloud filaments embedded in dense aerosol) or when observing diffuse features that occur only intermittently (e.g., volcanic ash in the tropical tropopause layer). The two methods examined in this study establish overall classification correctness boundaries due to their differing algorithm uncertainties. In addition to comparing the outputs from the two algorithms, analysis of sampling, data training, performance measurements, fuzzy linear discriminants, defuzzification, error propagation, and key parameters in feature type discrimination with the FKM method are further discussed in order to better understand the utility and limits of the application of clustering algorithms to space lidar measurements. In general, we find that both FKM and COCA classification uncertainties are only minimally affected by noise in the CALIPSO measurements, though both algorithms can be challenged by especially complex scenes containing mixtures of discrete layer types. Our analysis results show that attenuated backscatter and color ratio are the driving factors that separate water clouds from aerosols; backscatter intensity, depolarization, and mid-layer altitude are most useful in discriminating between aerosols and ice clouds; and the joint distribution of backscatter intensity and depolarization ratio is critically important for distinguishing ice clouds from water clouds.

Zeng, Shan↗
NASA NTRS1981

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

Supervised and cluster block training statistics were used to analyze the thematic mapper simulation MSS data (both 1979 and 1980 data sets). Cover information classes identified on SAR imagery include: hardwood, pine, mixed pine hardwood, clearcut, pasture, crops, emergent crops, bare soil, urban, and water. Preliminary analysis of the HH and HV polarized SAR data indicate a high variance associated with each information class except for water and bare soil. The large variance for most spectral classes suggests that while the means might be statistically separable, an overlap may exist between the classes which could introduce a significant classification error. The quantitative values of many cover types are much larger on the HV polarization than on the HH, thereby indicating the relative nature of the digitized data values. The mean values of the spectral classes in the areas with larger look angles are greater than the means of the same cover type in other areas having steeper look angles. Difficulty in accurately overlaying the dual polarization of the SAR data was resolved.

Hoffer, R. M.↗
NASA NTRS2010

Analyzing AQP Data to Improve Electronic Flight Bag (EFB) Operations and Training

Key points include: Initiate data collection and analysis early in the implementation process. Use data to identify procedural and training refinements. Use a de-identified system to analyze longitudinal data. Use longitudinal I/E data to improve their standardization. Identify above average pilots and crews and use their performance to specify best practices. Analyze below average crew performance data to isolate problems with the training, evaluator standardization and pilot proficiency.

Seamster, Thomas L.↗
NASA NTRS1975

M-DAS: System for multispectral data analysis

M-DAS is a ground data processing system designed for analysis of multispectral data. M-DAS operates on multispectral data from LANDSAT, S-192, M2S and other sources in CCT form. Interactive training by operator-investigators using a variable cursor on a color display was used to derive optimum processing coefficients and data on cluster separability. An advanced multivariate normal-maximum likelihood processing algorithm was used to produce output in various formats: color-coded film images, geometrically corrected map overlays, moving displays of scene sections, coverage tabulations and categorized CCTs. The analysis procedure for M-DAS involves three phases: (1) screening and training, (2) analysis of training data to compute performance predictions and processing coefficients, and (3) processing of multichannel input data into categorized results. Typical M-DAS applications involve iteration between each of these phases. A series of photographs of the M-DAS display are used to illustrate M-DAS operation.

Johnson, R. H.↗