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NASA's Aviation Safety and Modeling Project

Capabilities for automated analysis of flight data are under development. The Aviation Safety Monitoring and Modeling (ASMM) Project of NASA’s Aviation Safety program is cultivating sources of data and developing automated computer hardware and software to facilitate efficient, comprehensive, and accurate analyses of the data collected from large, heterogeneous databases throughout the national aviation system. The ASMM addresses the need to provide means for increasing safety by enabling the identification and correcting of predisposing conditions that could lead to accidents or to incidents that pose aviation risks.

Posted in: Briefs, TSP

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Reducing Centroid Error Through Model-Based Noise Reduction

Corrections are made for bias and noise.A method of processing the digitized output of a charge-coupled device (CCD) image detector has been devised to enable reduction of the error in computed centroid of the image of a point source of light. The method involves model-based estimation of, and correction for, the contributions of bias and noise to the image data. The method could be used to advantage in any of a variety of applications in which there are requirements for measuring precise locations of, and/or precisely aiming optical instruments toward, point light sources.

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Integrating Terrain Maps Into a Reactive Navigation Strategy

Traversability of terrain is taken into account as an integral part of navigation. An improved method of processing information for autonomous navigation of a robotic vehicle across rough terrain involves the integration of terrain maps into a reactive navigation strategy. Somewhat more precisely, the method involves the incorporation, into navigation logic, of data equivalent to regional traversability maps. The terrain characteristic is mapped using a fuzzy-logic representation of the difficulty of traversing the terrain. The method is robust in that it integrates a global path-planning strategy with sensor-based regional and local navigation strategies to ensure a high probability of success in reaching a destination and avoiding obstacles along the way. The sensor-based strategies use cameras aboard the vehicle to observe the regional terrain, defined as the area of the terrain that covers the immediate vicinity near the vehicle to a specified distance a few meters away. The method at an earlier stage of development was described in “Navigating a Mobile Robot Across Terrain Using Fuzzy Logic” (), NASA Tech Briefs, Vol. 27, No. 2 (February 2003), page 5a. A recent update on the terrain classification stage of the method was reported in “Quantifying Traversability of Terrain for a Mobile Robot” (), NASA Tech Briefs, Vol. 29, No. 7 (July 2005), page 56. To recapitulate: The basic building blocks of the method are three behaviors that focus on successively smaller spatial scales and are integrated (in the sense of blended) through gains or weighting factors to generate speed and steering commands. The weighting factors are generated by fuzzy logic rules that take account of the current status of the vehicle.

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Adaptive Modeling Language and Its Derivatives

Modeling language enables automation of the entire product development cycle.Adaptive Modeling Language (AML), developed by TechnoSoft, Inc., is the underlying language of an object-oriented, multidisciplinary, knowledge-based engineering framework. TechnoSoft is a leading provider of object-oriented modeling and simulation technology used for commercial and defense applications. AML offers an advanced modeling paradigm with an open architecture, enabling the automation of the entire product development cycle, integrating product configuration, design, analysis, visualization, production planning, inspection, and cost estimation.

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Education and Training Module in Alertness Management

An interactive Web-based General Aviation version of the module is now available for FAA WINGS credit. The education and training module (ETM) in alertness management has now been integrated as part of the training regimen of the Pilot Proficiency Awards Program (“WINGS”) of the Federal Aviation Administration. Originated and now maintained current by the Fatigue Countermeasures Group at NASA Ames Research Center, the ETM in Alertness Management is designed to give pilots the benefit of the best and most recent research on the basics of sleep physiology, the causes of fatigue, and strategies for managing alertness during flight operations.

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Generation of Data-Rate Profiles of Ka-Band Deep-Space Links

A short report discusses a methodology for designing Ka-band Deep-Space-to-Earth radio-communication links. This methodology is oriented toward minimizing the effects of weather on the Ka-band telecommunication link by maximizing the expected data return subject to minimum link availability and a limited number of data rates. This methodology differs from the current standard practices in which a link is designed according to a margin policy for a given link availability at 10° elevation. In this methodology, one chooses a data-rate profile that will maximize the average data return over a pass while satisfying a minimum- availability requirement for the pass, subject to mission operational limititations expressed in terms of the number of data rates used during the pass. The methodology is implemented in an intelligent search algorithm that first finds the allowable datarate profiles from the mission constraints, spacecraft-to-Earth distance, spacecraft EIRP (effective isotropic radiated power), and the applicable zenith atmospheric noise temperature distribution, and then selects the best data rate in terms of maximum average data return from the set of allowable data-rate profiles.

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Methodology for Designing Fault-Protection Software

A document describes a methodology for designing fault-protection (FP) software for autonomous spacecraft. The methodology embodies and extends established engineering practices in the technical discipline of Fault Detection, Diagnosis, Mitigation, and Recovery; and has been successfully implemented in the Deep Impact Spacecraft, a NASA Discovery mission. Based on established concepts of Fault Monitors and Responses, this FP methodology extends the notion of Opinion, Symptom, Alarm (aka Fault), and Response with numerous new notions, sub-notions, software constructs, and logic and timing gates. For example, Monitor generates a RawOpinion, which graduates into Opinion, categorized into no-opinion, acceptable, or unacceptable opinion. RaiseSymptom, ForceSymptom, and ClearSymptom govern the establishment and then mapping to an Alarm (aka Fault). Local Response is distinguished from FP System Response. A 1-to-n and n-to-1 mapping is established among Monitors, Symptoms, and Responses. Responses are categorized by device versus by function. Responses operate in tiers, where the early tiers attempt to resolve the Fault in a localized step-bystep fashion, relegating more system-level response to later tier(s). Recovery actions are gated by epoch recovery timing, enabling strategy, urgency, MaxRetry gate, hardware availability, hazardous versus ordinary fault, and many other priority gates. This methodology is systematic, logical, and uses multiple linked tables, parameter files, and recovery command sequences. The credibility of the FP design is proven via a fault-tree analysis “top-down” approach, and a functional fault-mode-effects-andanalysis via “bottoms-up” approach. Via this process, the mitigation and recovery strategy( s) per Fault Containment Region scope (width versus depth) the FP architecture.

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