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NASA NTRS2014

Evolution of a Reconfigurable Processing Platform for a Next Generation Space Software Defined Radio

The National Aeronautics and Space Administration (NASA)Harris Ka-Band Software Defined Radio (SDR) is the first, fully reprogrammable space-qualified SDR operating in the Ka-Band frequency range. Providing exceptionally higher data communication rates than previously possible, this SDR offers in-orbit reconfiguration, multi-waveform operation, and fast deployment due to its highly modular hardware and software architecture. Currently in operation on the International Space Station (ISS), this new paradigm of reconfigurable technology is enabling experimenters to investigate navigation and networking in the space environment.The modular SDR and the NASA developed Space Telecommunications Radio System (STRS) architecture standard are the basis for Harris reusable, digital signal processing space platform trademarked as AppSTAR. As a result, two new space radio products are a synthetic aperture radar payload and an Automatic Detection Surveillance Broadcast (ADS-B) receiver. In addition, Harris is currently developing many new products similar to the Ka-Band software defined radio for other applications. For NASAs next generation flight Ka-Band radio development, leveraging these advancements could lead to a more robust and more capable software defined radio.The space environment has special considerations different from terrestrial applications that must be considered for any system operated in space. Each space mission has unique requirements that can make these systems unique. These unique requirements can make products that are expensive and limited in reuse. Space systems put a premium on size, weight and power. A key trade is the amount of reconfigurability in a space system. The more reconfigurable the hardware platform, the easier it is to adapt to the platform to the next mission, and this reduces the amount of non-recurring engineering costs. However, the more reconfigurable platforms often use more spacecraft resources. Software has similar considerations to hardware. Having an architecture standard promotes reuse of software and firmware. Space platforms have limited processor capability, which makes the trade on the amount of amount of flexibility paramount.

Telecommunication↗
NASA NTRS2015

Lidar Sensor Performance in Closed-Loop Flight Testing of the Morpheus Rocket-Propelled Lander to a Lunar-Like Hazard Field

For the first time, a suite of three lidar sensors have been used in flight to scan a lunar-like hazard field, identify a safe landing site, and, in concert with an experimental Guidance, Navigation, and Control (GN&C) system, help to guide the Morpheus autonomous, rocket-propelled, free-flying lander to that safe site on the hazard field. The lidar sensors and GN&C system are part of the Autonomous Precision Landing and Hazard Detection and Avoidance Technology (ALHAT) project which has been seeking to develop a system capable of enabling safe, precise crewed or robotic landings in challenging terrain on planetary bodies under any ambient lighting conditions. The 3-D imaging Flash Lidar is a second generation, compact, real-time, aircooled instrument developed from a number of components from industry and NASA and is used as part of the ALHAT Hazard Detection System (HDS) to scan the hazard field and build a 3-D Digital Elevation Map (DEM) in near-real time for identifying safe sites. The Flash Lidar is capable of identifying a 30 cm hazard from a slant range of 1 km with its 8 cm range precision (1-s). The Flash Lidar is also used in Hazard Relative Navigation (HRN) to provide position updates down to a 250m slant range to the ALHAT navigation filter as it guides Morpheus to the safe site. The Navigation Doppler Lidar (NDL) system has been developed within NASA to provide velocity measurements with an accuracy of 0.2 cm/sec and range measurements with an accuracy of 17 cm both from a maximum range of 2,200 m to a minimum range of several meters above the ground. The NDLâ€"TM"s measurements are fed into the ALHAT navigation filter to provide lander guidance to the safe site. The Laser Altimeter (LA), also developed within NASA, provides range measurements with an accuracy of 5 cm from a maximum operational range of 30 km down to 1 m and, being a separate sensor from the Flash Lidar, can provide range along a separate vector. The LA measurements are also fed into the ALHAT navigation filter to provide lander guidance to the safe site. The flight tests served as the culmination of the TRL 6 journey for the ALHAT system and included launch from a pad situated at the NASA-Kennedy Space Center Shuttle Landing Facility (SLF) runway, a lunar-like descent trajectory from an altitude of 250m, and landing on a lunar-like hazard field of rocks, craters, hazardous slopes, and safe sites 400m down-range just off the North end of the runway. The tests both confirmed the expected performance and also revealed several challenges present in the flight-like environment which will feed into future TRL advancement of the sensors. Guidance provided by the ALHAT system was impeded in portions of the trajectory and intermittent near the end of the trajectory due to optical effects arising from air heated by the rocket engine. The Flash Lidar identified hazards as small as 30 cm from the maximum slant range of 450 m which Morpheus could provide; however, it was occasionally susceptible to an increase in range noise due to scintillation arising from air heated by the Morpheus rocket engine which entered its Field-of-View (FOV). The Flash Lidar was also susceptible to pre-triggering, during the HRN phase, on a dust cloud created during launch and transported down-range by the wind. The NDL provided velocity and range measurements to the expected accuracy levels yet it was also susceptible to signal degradation due to air heated by the rocket engine. The LA, operating with a degraded transmitter laser, also showed signal attenuation over a few seconds at a specific phase of the flight due to the heat plume generated by the rocket engine.

Roback, V. Eric↗
NASA NTRS2008

A Topographical Lidar System for Terrain-Relative Navigation

An imaging lidar system is being developed for use in navigation, relative to the local terrain. This technology will potentially be used for future spacecraft landing on the Moon. Systems like this one could also be used on Earth for diverse purposes, including mapping terrain, navigating aircraft with respect to terrain and military applications. The system has been field-tested aboard a helicopter in the Mojave Desert. When this system was designed, digitizers with sufficient sampling rate (2 GHz) were only available with very limited memory. Also, it was desirable to limit the amount of data to be transferred between the digitizer and the mass storage between individual frames. One of the novelty design features of this system was to design the system around the limited amount of memory of the digitizer. The system is required to operate over an altitude (distance) range from a few meters to approximately 1 km, but for each scan across the full field of view, the digitizer memory is only able to hold data for an altitude range no more than 100 m. Data acquisition methods in support of the limited 100 m wide altitude range are described.

Liebe, Carl Christian↗
NASA NTRSDate not supplied

GeoAI advances in specific landform mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation. References: Arundel, Samantha T., Wenwen Li, and Sizhe Wang. 2020. “GeoNat v1.0: A Dataset for Natural Feature Mapping with Artificial Intelligence and Supervised Learning.” Transactions in GIS 24 (3): 556–72. https://doi.org/10.1111/tgis.12633. Arundel, Samantha T, and Gaurav Sinha. 2018. “Validating GEOBIA Based Terrain Segmentation and Classification for Automated Delineation of Cognitively Salient Landforms BT - Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017).” In Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017), Lecture Notes in Geoinformation and Cartography, edited by Paolo Fogliaroni, Andrea Ballatore, and Eliseo Clementini, 9–14. Cham: Springer International Publishing. Arundel, Samantha T., Gaurav Sinha, Wenwen Li, David P. Martin, Kevin G. McKeehan, and Philip T. Thiem. 2023. “Historical Maps Inform Landform Cognition in Machine Learning.” Abstracts of the ICA 6 (August): 1–2. https://doi.org/10.5194/ica-abs-6-10-2023. Evans, Ian S. 2012. “Geomorphometry and Landform Mapping: What Is a Landform?” Geomorphology 137 (1): 94–106. https://doi.org/10.1016/j.geomorph.2010.09.029.

machine learning↗
NASA NTRSDate not supplied

GeoAI Advances in Specific Landform Mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation.

machine learning↗
NASA NTRS2024

Digital Lunar Exploration Sites (DLES) Terrain Crafting

Humans will soon be returning to the surface of the Moon with NASA’s Artemis program. The Artemis program is an international collaboration that will consist of a complex series of space systems and missions to explore the lunar surface and pave the way for the future exploration of Mars. NASA and its partners rely heavily on simulation for lighting and navigation studies as well as training astronauts, flight controllers, and mission support staff. The NASA Exploration Systems Simulations (NExSyS) team in the Simulation and Graphics Branch (ER7) in the Engineering Directorate at NASA’s Johnson Space Center has built up many simulation products to support this effort, one of which is the Digital Lunar Exploration Sites (DLES). DLES is a collection of products used to simulate and render the lunar surface in a digital environment. We discussed and presented an overview of the DLES products at the 2022 IEEE Aerospace Conference in Big Sky, MT with a paper titled "Digital Lunar Exploration Sites". This “DLES Terrain Crafting” paper will expand on the information previously provided in “DLES” paper and dive deeper into the details of the terrain crafting process and the toolsets used to support this task. The best digital data currently available of the lunar surface is provided by the Lunar Reconnaissance Orbiter (LRO). Its Lunar Orbiter Laser Altimeter (LOLA) achieves an impressive resolution of 5m per pixel at the Lunar South Pole (LSP) and can generate datasets covering a large continuous region near the LSP. There are a few additional methods, such as Shape from Shading which can infer higher resolution data (up to 1m per pixel) from the LRO Narrow Angle Camera (NAC) images. However, surface-based simulations require higher-resolution data, and this paper will discuss the process of enhancing the terrain to meet that need. The process begins with capturing statistical data of craters in the regions of interest using images provided by the LRO NAC. This data is then used to scatter artificial features which are not captured in the truth data, resulting in an enhanced DEM with a much higher resolution of 20cm per pixel. Many tools were built up to assist in the creation of these artificial Digital Elevation Models (DEM), which this paper will discuss in detail. DEMs themselves are a very powerful representation of a planetary surface, and many operations and tools can utilize the data they contain. This paper includes a description of the rendering of the lunar surface in a graphics engine, generation of contact patches to simulate tire to ground interaction, and ray tracing utilities to model Line of Sight (LOS) interactions with the terrain. This paper will also explore some new tool sets currently under development which aim to utilize Machine Learning (ML) to assist in the identification of craters from LRO NAC imagery. While this is not a novel idea, the NExSyS team is developing a unique approach which may result in more robust identification of crater characteristics.

Artemis↗
NASA NTRS2015

Lidar Sensor Performance in Closed-Loop Flight Testing of the Morpheus Rocket-Propelled Lander to a Lunar-Like Hazard Field

For the first time, a suite of three lidar sensors have been used in flight to scan a lunar-like hazard field, identify a safe landing site, and, in concert with an experimental Guidance, Navigation, and Control (GN&C) system, guide the Morpheus autonomous, rocket-propelled, free-flying test bed to a safe landing on the hazard field. The lidar sensors and GN&C system are part of the Autonomous Precision Landing and Hazard Detection and Avoidance Technology (ALHAT) project which has been seeking to develop a system capable of enabling safe, precise crewed or robotic landings in challenging terrain on planetary bodies under any ambient lighting conditions. The 3-D imaging flash lidar is a second generation, compact, real-time, air-cooled instrument developed from a number of cutting-edge components from industry and NASA and is used as part of the ALHAT Hazard Detection System (HDS) to scan the hazard field and build a 3-D Digital Elevation Map (DEM) in near-real time for identifying safe sites. The flash lidar is capable of identifying a 30 cm hazard from a slant range of 1 km with its 8 cm range precision at 1 sigma. The flash lidar is also used in Hazard Relative Navigation (HRN) to provide position updates down to a 250m slant range to the ALHAT navigation filter as it guides Morpheus to the safe site. The Doppler Lidar system has been developed within NASA to provide velocity measurements with an accuracy of 0.2 cm/sec and range measurements with an accuracy of 17 cm both from a maximum range of 2,200 m to a minimum range of several meters above the ground. The Doppler Lidar's measurements are fed into the ALHAT navigation filter to provide lander guidance to the safe site. The Laser Altimeter, also developed within NASA, provides range measurements with an accuracy of 5 cm from a maximum operational range of 30 km down to 1 m and, being a separate sensor from the flash lidar, can provide range along a separate vector. The Laser Altimeter measurements are also fed into the ALHAT navigation filter to provide lander guidance to the safe site. The flight tests served as the culmination of the TRL 6 journey for the lidar suite and included launch from a pad situated at the NASA-Kennedy Space Center Shuttle Landing Facility (SLF) runway, a lunar-like descent trajectory from an altitude of 250m, and landing on a lunar-like hazard field of rocks, craters, hazardous slopes, and safe sites 400m down-range just off the North end of the runway. The tests both confirmed the expected performance and also revealed several challenges present in the flight-like environment which will feed into future TRL advancement of the sensors. The flash lidar identified hazards as small as 30 cm from the maximum slant range of 450 m which Morpheus could provide, however, it was occasionally susceptible to an increase in range noise due to heated air from the Morpheus rocket plume which entered its Field-of-View (FOV). The flash lidar was also susceptible to pre-triggering on dust during the HRN phase which was created during launch and transported by the wind. The Doppler Lidar provided velocity and range measurements to the expected accuracy levels yet it was also susceptible to signal degradation due to air heated by the rocket engine. The Laser Altimeter, operating with a degraded transmitter laser, also showed signal attenuation over a few seconds at a specific phase of the flight due to the heat plume generated by the rocket engine.

Roback, Vincent E.↗
NASA NTRS2012

Projection of Stabilized Aerial Imagery Onto Digital Elevation Maps for Geo-Rectified and Jitter-Free Viewing

As imagery is collected from an airborne platform, an individual viewing the images wants to know from where on the Earth the images were collected. To do this, some information about the camera needs to be known, such as its position and orientation relative to the Earth. This can be provided by common inertial navigation systems (INS). Once the location of the camera is known, it is useful to project an image onto some representation of the Earth. Due to the non-smooth terrain of the Earth (mountains, valleys, etc.), this projection is highly non-linear. Thus, to ensure accurate projection, one needs to project onto a digital elevation map (DEM). This allows one to view the images overlaid onto a representation of the Earth. A code has been developed that takes an image, a model of the camera used to acquire that image, the pose of the camera during acquisition (as provided by an INS), and a DEM, and outputs an image that has been geo-rectified. The world coordinate of the bounds of the image are provided for viewing purposes. The code finds a mapping from points on the ground (DEM) to pixels in the image. By performing this process for all points on the ground, one can "paint" the ground with the image, effectively performing a projection of the image onto the ground. In order to make this process efficient, a method was developed for finding a region of interest (ROI) on the ground to where the image will project. This code is useful in any scenario involving an aerial imaging platform that moves and rotates over time. Many other applications are possible in processing aerial and satellite imagery.

Ansar, Adnan I.↗
NASA NTRS1973

Code-regenerative clean-up loop for a ranging transponder

Digital processing system phase locks on received ranging signal and creates clean replica of received ranging code. System is broadly applicable to variety of terrestrial ranging problems, including oceanic navigation.

Hurd, W. J.↗
NASA NTRS1986

Practical design of magnetic heading sensors

Although fluxgate magnetometers have been in use for many years, and a number of theoretical studies of these sensors are available, very little information has been published on the engineering choices relating to the detailed design of magnetometers intended for navigational applications in aircraft and other vehicles. This paper addresses some of the problems encountered in the design of the sensing head, the choice of operating mode, and the design of the supporting electronics for a simple low-cost magnetometer system which will produce a digital readout of the magnetic heading of the vehicle in which it is mounted. A major goal of this study has been to develop a design which minimizes circuit complexity, is easy to fabricate and calibrate, and utilizes a minimum of specialized components. Such a magnetometer system is intended either as a stand-alone indicating instrument or as a source of magnetic heading information for a more sophisticated navigational system.

Garner, H. D.↗
NASA NTRS2006

AERCam Autonomy: Intelligent Software Architecture for Robotic Free Flying Nanosatellite Inspection Vehicles

The NASA Johnson Space Center has developed a nanosatellite-class Free Flyer intended for future external inspection and remote viewing of human spacecraft. The Miniature Autonomous Extravehicular Robotic Camera (Mini AERCam) technology demonstration unit has been integrated into the approximate form and function of a flight system. The spherical Mini AERCam Free Flyer is 7.5 inches in diameter and weighs approximately 10 pounds, yet it incorporates significant additional capabilities compared to the 35-pound, 14-inch diameter AERCam Sprint that flew as a Shuttle flight experiment in 1997. Mini AERCam hosts a full suite of miniaturized avionics, instrumentation, communications, navigation, power, propulsion, and imaging subsystems, including digital video cameras and a high resolution still image camera. The vehicle is designed for either remotely piloted operations or supervised autonomous operations, including automatic stationkeeping, point-to-point maneuvering, and waypoint tracking. The Mini AERCam Free Flyer is accompanied by a sophisticated control station for command and control, as well as a docking system for automated deployment, docking, and recharge at a parent spacecraft. Free Flyer functional testing has been conducted successfully on both an airbearing table and in a six-degree-of-freedom closed-loop orbital simulation with avionics hardware in the loop. Mini AERCam aims to provide beneficial on-orbit views that cannot be obtained from fixed cameras, cameras on robotic manipulators, or cameras carried by crewmembers during extravehicular activities (EVA s). On Shuttle or International Space Station (ISS), for example, Mini AERCam could support external robotic operations by supplying orthogonal views to the intravehicular activity (IVA) robotic operator, supply views of EVA operations to IVA and/or ground crews monitoring the EVA, and carry out independent visual inspections of areas of interest around the spacecraft. To enable these future benefits with minimal impact on IVA operators and ground controllers, the Mini AERCam system architecture incorporates intelligent systems attributes that support various autonomous capabilities. 1) A robust command sequencer enables task-level command scripting. Command scripting is employed for operations such as automatic inspection scans over a region of interest, and operator-hands-off automated docking. 2) A system manager built on the same expert-system software as the command sequencer provides detection and smart-response capability for potential system-level anomalies, like loss of communications between the Free Flyer and control station. 3) An AERCam dynamics manager provides nominal and off-nominal management of guidance, navigation, and control (GN&C) functions. It is employed for safe trajectory monitoring, contingency maneuvering, and related roles. This paper will describe these architectural components of Mini AERCam autonomy, as well as the interaction of these elements with a human operator during supervised autonomous control.

Fredrickson, Steven E.↗
NASA NTRS1992

Real-Time Connected-Element Radio Interferometry

In connected-element-interferometry (CEI), differences between times of arrival of signals at two antennas measured more accurately than in very-long-baseline interferometry (VLBI). Analog and digital fiber-optic links along relatively short baseline enable transmission of precise timing signals and real-time digital processing to determine phase delays between signals received at stations A and B. Baseline typically of order of tens of kilometers. Techniques adaptable to radio laboratories and navigation systems requiring distribution of precise time signals and accurate measurements of time.

Edwards, Charles D., Jr.↗
NASA NTRS2020

InSight Entry, Descent and Landing Pre-Flight Performance Predictions

On November 26, 2018, the Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) lander successfully touched down on the surface of Mars. Over its seven-plus year development, NASA Langley Research Center’s (LaRC) Program to Optimize Simulated Trajectories II (POST2) was used to assess the mission’s Entry, Descent and Landing (EDL) vehicle system performance against related requirements across the full range of possible environmental and spacecraft conditions. Much of the simulation code was derived from the Phoenix mission, for which this vehicle is very similar. The InSight six degree-of-freedom simulation included models for Mars atmosphere, gravity and digital elevation maps of the landing location. Additionally, vehicle specific aerodynamic, parachute, engine, navigation sensor, flight software and landing radar models were also included. A set of dispersions for each model, as well as for additional simulation input parameters, were also included in order to provide a statistical, Monte Carlo prediction of the EDL system performance. An overview of the pre-flight performance assessments completed, including the various simulation campaigns used, will be provided. Ultimately, this work was critical in the assessment of readiness for InSight launch. A brief description of the use of this simulation in support of flight operations is also discussed.

Robert W Maddock↗
NASA NTRS1999

Conformal, Transparent Printed Antenna Developed for Communication and Navigation Systems

Conformal, transparent printed antennas have advantages over conventional antennas in terms of space reuse and aesthetics. Because of their compactness and thin profile, these antennas can be mounted on video displays for efficient integration in communication systems such as palmtop computers, digital telephones, and flat-panel television displays. As an array of multiple elements, the antenna subsystem may save weight by reusing space (via vertical stacking) on photovoltaic arrays or on Earth-facing sensors. Also, the antenna could go unnoticed on automobile windshields or building windows, enabling satellite uplinks and downlinks or other emerging high-frequency communications.

Lee, Richard Q.↗
NASA NTRS1972

Study of aircraft centered navigation, guidance, and traffic situation system concept for terminal area operation

A concept for automating the control of air traffic in the terminal area in which the primary man-machine interface is the cockpit is described. The ground and airborne inputs required for implementing this concept are discussed. Digital data link requirements of 10,000 bits per second are explained. A particular implementation of this concept including a sequencing and separation algorithm which generates flight paths and implements a natural order landing sequence is presented. Onboard computer/display avionics utilizing a traffic situation display is described. A preliminary simulation of this concept has been developed which includes a simple, efficient sequencing algorithm and a complete aircraft dynamics model. This simulated jet transport was flown through automated terminal-area traffic situations by pilots using relatively sophisticated displays, and pilot performance and observations are discussed.

Anderson, W. W.↗
NASA NTRS1985

Technology research for strapdown inertial experiment and digital flight control and guidance

A helicopter flight-test program to evaluate the performance of Honeywell's Tetrad - a strapdown, laser gyro, inertial navitation system is discussed. The results of 34 flights showed a mean final navigational velocity error of 5.06 knots, with a standard deviation of 3.84 knots; a corresponding mean final position error of 2.66 n.mi., with a standard deviation of 1.48 n.m.; and a modeled mean-position-error growth rate for the 34 tests of 1.96 knots, with a standard deviation of 1.09 knots. Tetrad's four-ring laser gyros provided reliable and accurate angular rate sensing during the test program and on sensor failures were detected during the evaluation. Criteria suitable for investigating cockpit systems in rotorcraft were developed. This criteria led to the development of two basic simulators. The first was a standard simulator which could be used to obtain baseline information for studying pilot workload and interactions. The second was an advanced simulator which integrated the RODAAS developed by Honeywell into this simulator. The second area also included surveying the aerospace industry to determine the level of use and impact of microcomputers and related components on avionics systems.

Carestia, R. A.↗
NASA NTRS1976

Space shuttle digital flight control system

The space shuttle digital, fly by wire, flight control system presents an interesting challenge in avionics system design. In residence in each of four redundant general purpose computers at lift off are the guidance, navigation, and control algorithms for the entire flight. The mission is divided into several flight segments: first stage ascent, second stage ascent; abort to launch site, abort once around; on orbit operations, entry, terminal area energy management; and approach and landing. The FCS is complicated in that it must perform the functions to fly the shuttle as a boost vehicle, as a spacecraft, as a reentry vehicle, and as a conventional aircraft. The crew is provided with both manual and automatic modes of operations in all flight phases including touchdown and rollout.

Minott, G. M.↗
NASA NTRS1978

Basic avionics module design for general aviation aircraft

The design of an advanced digital avionics system (basic avionics module) for general aviation aircraft operated with a single pilot under IFR conditions is described. The microprocessor based system provided all avionic functions, including flight management, navigation, and lateral flight control. The mode selection was interactive with the pilot. The system used a navigation map data base to provide operation in the current and planned air traffic control environment. The system design included software design listings for some of the required modules. The distributed microcomputer uses the IEEE 488 bus for interconnecting the microcomputer and sensors.

Smyth, R. K.↗