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NASA NTRS2012

Lunar Navigation Determination System - LaNDS

A portable comprehensive navigational system has been developed that both robotic and human explorers can use to determine their location, attitude, and heading anywhere on the lunar surface independent of external infrastructure (needs no Lunar satellite network, line of sight to the Sun or Earth, etc.). The system combines robust processing power with an extensive topographical database to create a real-time atlas (GIS Geospatial Information System) that is able to autonomously control and monitor both single unmanned rovers and fleets of rovers, as well as science payload stations. The system includes provisions for teleoperation and tele-presence. The system accepts (but does not require) inputs from a wide range of sensors. A means was needed to establish a location when the search is taken deep in a crater (looking for water ice) and out of view of Earth or any other references. A star camera can be employed to determine the user's attitude in menial space and stellar map in body space. A local nadir reference (e.g., an accelerometer that orients the nadir vector in body space) can be used in conjunction with a digital ephemeris and gravity model of the Moon to isolate the latitude, longitude, and azimuth of the user on the surface. That information can be used in conjunction with a Lunar GIS and advanced navigation planning algorithms to aid astronauts (or other assets) to navigate on the Lunar surface.

Quinn, David↗
NASA NTRSDate not supplied

Adaptive Stress Testing of Collision Avoidance Systems for Small UASs with Deep Reinforcement Learning

The next-generation Airborne Collision Avoidance System for smaller UASs (ACAS sXu) is currently being developed and tested by the Federal Aviation Administration (FAA) to provide detect-and-avoid capability for small unmanned aircraft operating beyond line-of-sight. Due to the complexity and safety-critical nature of the system, safety validation is important not only for the certification of the final system, but also for informing changes during the iterative development process. In this paper, we analyze a prototype of ACAS sXu in simulated aircraft encounters to discover scenarios of small near mid-air collisions (sNMACs), an important safety event in which two aircraft come closer than 50 feet horizontally and 15 feet vertically. Due to the size and complexity of the system as well as rarity of sNMAC events, traditional methods such as Monte Carlo testing often require informed setup and targeting to elicit failures. However, such a dependence on domain knowledge can be incompatible with the independent verification and validation (IV&V) process, the aim of which is to discover unforeseen issues. To address these challenges, we apply an accelerated validation method called adaptive stress testing (AST) to find the most likely sNMAC scenarios without reliance on system introspection. AST uses reinforcement learning to adapt the search towards the most promising areas of the search space as it progresses. We use a state-of-the-art deep reinforcement learning algorithm, proximate policy optimization, to more efficiently search the large and continuous state space. We find that this approach significantly improves the performance of AST compared to a prior approach based on Monte Carlo tree search. We perform experiments using AST to find sNMAC events under various encounter configurations, varying parameters pertaining to dynamics and coordination. Our experiments show AST to be very effective at finding sNMAC scenarios. We summarize our findings, presenting high-level categories of discovered sNMACs and specific examples of encounters in each category.

aircraft collision avoidance↗
NASA NTRS2000

Optimization of Microelectronic Devices for Sensor Applications

The NASA/JPL goal to reduce payload in future space missions while increasing mission capability demands miniaturization of active and passive sensors, analytical instruments and communication systems among others. Currently, typical system requirements include the detection of particular spectral lines, associated data processing, and communication of the acquired data to other systems. Advances in lithography and deposition methods result in more advanced devices for space application, while the sub-micron resolution currently available opens a vast design space. Though an experimental exploration of this widening design space-searching for optimized performance by repeated fabrication efforts-is unfeasible, it does motivate the development of reliable software design tools. These tools necessitate models based on fundamental physics and mathematics of the device to accurately model effects such as diffraction and scattering in opto-electronic devices, or bandstructure and scattering in heterostructure devices. The software tools must have convenient turn-around times and interfaces that allow effective usage. The first issue is addressed by the application of high-performance computers and the second by the development of graphical user interfaces driven by properly developed data structures. These tools can then be integrated into an optimization environment, and with the available memory capacity and computational speed of high performance parallel platforms, simulation of optimized components can proceed. In this paper, specific applications of the electromagnetic modeling of infrared filtering, as well as heterostructure device design will be presented using genetic algorithm global optimization methods.

Cwik, Tom↗
NASA NTRS2008

NASA Tech Briefs, November 2008

Topics covered include: Digital Phase Meter for a Laser Heterodyne Interferometer; Vision System Measures Motions of Robot and External Objects; Advanced Precipitation Radar Antenna to Measure Rainfall From Space; Wide-Band Radar for Measuring Thickness of Sea Ice; Vertical Isolation for Photodiodes in CMOS Imagers; Wide-Band Microwave Receivers Using Photonic Processing; L-Band Transmit/Receive Module for Phase-Stable Array Antennas; Microwave Power Combiner/Switch Utilizing a Faraday Rotator; Compact Low-Loss Planar Magic-T; Using Pipelined XNOR Logic to Reduce SEU Risks in State Machines; Quasi-Optical Transmission Line for 94-GHz Radar; Next Generation Flight Controller Trainer System; Converting from DDOR SASF to APF; Converting from CVF to AAF; Documenting AUTOGEN and APGEN Model Files; Sequence History Update Tool; Extraction and Analysis of Display Data; MRO DKF Post-Processing Tool; Rig Diagnostic Tools; MRO Sequence Checking Tool; Science Activity Planner for the MER Mission; UAVSAR Flight-Planning System; Templates for Deposition of Microscopic Pointed Structures; Adjustable Membrane Mirrors Incorporating G-Elastomers; Hall-Effect Thruster Utilizing Bismuth as Propellant; High-Temperature Crystal-Growth Cartridge Tubes Made by VPS; Quench Crucibles Reinforced with Metal; Deep-Sea Hydrothermal-Vent Sampler; Mars Rocket Propulsion System; Two-Stage Passive Vibration Isolator; Improved Thermal Design of a Compression Mold; Enhanced Pseudo-Waypoint Guidance for Spacecraft Maneuvers; Altimetry Using GPS-Reflection/Occultation Interferometry; Thermally Driven Josephson Effect; Perturbation Effects on a Supercritical C7H16/N2 Mixing Layer; Gold Nanoparticle Labels Amplify Ellipsometric Signals; Phase Matching of Diverse Modes in a WGM Resonator; WGM Resonators for Terahertz-to-Optical Frequency Conversion; Determining Concentration of Nanoparticles from Ellipsometry; Microwave-to-Optical Conversion in WGM Resonators; Four-Pass Coupler for Laser-Diode-Pumped Solid-State Laser; Low-Resolution Raman-Spectroscopy Combustion Thermometry; Temperature Sensors Based on WGM Optical Resonators; Varying the Divergence of Multiple Parallel Laser Beams; Efficient Algorithm for Rectangular Spiral Search; Algorithm-Based Fault Tolerance Integrated with Replication; Targeting and Localization for Mars Rover Operations; Terrain-Adaptive Navigation Architecture; Self-Adjusting Hash Tables for Embedded Flight Applications; Schema for Spacecraft-Command Dictionary; Combined GMSK Communications and PN Ranging; System-Level Integration of Mass Memory; Network-Attached Solid-State Recorder Architecture; Method of Cross-Linking Aerogels Using a One-Pot Reaction Scheme; An Efficient Reachability Analysis Algorithm.

Source record↗
NASA NTRS2016

Investigating the Simulink Auto-Coding Process

Model based program design is the most clear and direct way to develop algorithms and programs for interfacing with hardware. While coding "by hand" results in a more tailored product, the ever-growing size and complexity of modern-day applications can cause the project work load to quickly become unreasonable for one programmer. This has generally been addressed by splitting the product into separate modules to allow multiple developers to work in parallel on the same project, however this introduces new potentials for errors in the process. The fluidity, reliability and robustness of the code relies on the abilities of the programmers to communicate their methods to one another; furthermore, multiple programmers invites multiple potentially differing coding styles into the same product, which can cause a loss of readability or even module incompatibility. Fortunately, Mathworks has implemented an auto-coding feature that allows programmers to design their algorithms through the use of models and diagrams in the graphical programming environment Simulink, allowing the designer to visually determine what the hardware is to do. From here, the auto-coding feature handles converting the project into another programming language. This type of approach allows the designer to clearly see how the software will be directing the hardware without the need to try and interpret large amounts of code. In addition, it speeds up the programming process, minimizing the amount of man-hours spent on a single project, thus reducing the chance of human error as well as project turnover time. One such project that has benefited from the auto-coding procedure is Ramses, a portion of the GNC flight software on-board Orion that has been implemented primarily in Simulink. Currently, however, auto-coding Ramses into C++ requires 5 hours of code generation time. This causes issues if the tool ever needs to be debugged, as this code generation will need to occur with each edit to any part of the program; additionally, this is lost time that could be spent testing and analyzing the code. This is one of the more prominent issues with the auto-coding process, and while much information is available with regard to optimizing Simulink designs to produce efficient and reliable C++ code, not much research has been made public on how to reduce the code generation time. It is of interest to develop some insight as to what causes code generation times to be so significant, and determine if there are architecture guidelines or a desirable auto-coding configuration set to assist in streamlining this step of the design process for particular applications. To address the issue at hand, the Simulink coder was studied at a foundational level. For each different component type made available by the software, the features, auto-code generation time, and the format of the generated code were analyzed and documented. Tools were developed and documented to expedite these studies, particularly in the area of automating sequential builds to ensure accurate data was obtained. Next, the Ramses model was examined in an attempt to determine the composition and the types of technologies used in the model. This enabled the development of a model that uses similar technologies, but takes a fraction of the time to auto-code to reduce the turnaround time for experimentation. Lastly, the model was used to run a wide array of experiments and collect data to obtain knowledge about where to search for bottlenecks in the Ramses model. The resulting contributions of the overall effort consist of an experimental model for further investigation into the subject, as well as several automation tools to assist in analyzing the model, and a reference document offering insight to the auto-coding process, including documentation of the tools used in the model analysis, data illustrating some potential problem areas in the auto-coding process, and recommendations on areas or practices in the current Ramses model that should be further investigated. Several skills were required to be built up over the course of the internship project. First and foremost, my Simulink skills have improved drastically, as much of my experience had been modeling electronic circuits as opposed to software models. Furthermore, I am now comfortable working with the Simulink Auto-coder, a tool I had never used until this summer; this tool also tested my critical thinking and C++ knowledge as I had to interpret the C++ code it was generating and attempt to understand how the Simulink model affected the generated code. I had come into the internship with a solid understanding of Matlab code, but had done very little in using it to automate tasks, particularly Simulink tasks; along the same lines, I had rarely used shell script to automate and interface with programs, which I gained a fair amount of experience with this summer, including how to use regular expression. Lastly, soft-skills are an area everyone can continuously improve on; having never worked with NASA engineers, which to me seem to be a completely different breed than what I am used to (commercial electronic engineers), I learned to utilize the wealth of knowledge present at JSC. I wish I had come into the internship knowing exactly how helpful everyone in my branch would be, as I would have picked up on this sooner. I hope that having gained such a strong foundation in Simulink over this summer will open the opportunity to return to work on this project, or potentially other opportunities within the division. The idea of leaving a project I devoted ten weeks to is a hard one to cope with, so having the chance to pick up where I left off sounds appealing; alternatively, I am interested to see if there are any opening in the future that would allow me to work on a project that is more in-line with my research in estimation algorithms. Regardless, this summer has been a milestone in my professional career, and I hope this has started a long-term relationship between JSC and myself. I really enjoy the thought of building on my experience here over future summers while I work to complete my PhD at Missouri University of Science and Technology.

Gualdoni, Matthew J.↗
NASA NTRS2007

Exploring and Visualizing A-Train Instrument Data

The succession of US and international satellites that follow each other in close succession, known as the A-Train, affords an opportunity to atmospheric researchers that no single platform could provide: Increasing the number of observations at any given geographic location.. . a more complete "virtual science platform". However, vertically and horizontally, co-registering and regridding datasets from independently developed missions, Aqua, Calipso, Cloudsat, Parasol, and Aura, so that they can be inter-compared can be daunting to some, and may be repeated by many. Scientists will individually spend much of their time and resources acquiring A-Train datasets of interest residing at various locations, developing algorithms to match up and graph datasets along the A-Train track, and search through large amounts of data for areas and/or phenomena of interest. The aggregate amount of effort that can be expended on repeating pre-science tasks could climb into the tens of millions of dollars. The goal of the A-Train Data Depot (ATDD) is to enable free movement of remotely located A-Train data so that they are combined to create a consolidated vertical view of the Earth's Atmosphere along the A-Train tracks. The innovative approach of analyzing and visualizing atmospheric profiles along the platforms track (i.e., time) is accomplished by through the ATDDs Giovanni data analysis and visualization tool. Giovanni brings together data from Aqua (MODIS, AIRS, AMSR-E), Cloudsat (cloud profiling radar) and Calipso (CALIOP, IIR), as well as the Aura (OMI, MLS, HIRDLS, TES) to create a consolidated vertical view of the Earth's Atmosphere along the A-Train tracks. This easy to learn and use exploration tool will allow users to create vertical profiles of any desired A-Train dataset, for any given time of choice. This presentation shows the power of Giovanni by describing and illustrating how this tool facilitates and aids A-Train science and research. A web based display system Giovanni provides users with the capability of creating co-located profile images of temperature and humidity data from the MODIS, MLS and AIRS instruments for a user specified time and spatial area. In addition, Cloud and Aerosol profiles may also be displayed for the Cloudsat and Caliop instruments. The ability to modify horizontal and vertical axis range, data range and dynamic color range is also provided. Two dimensional strip plots of MODIS, AIRS, OM1 and POLDER parameters, co-located along the Cloudsat reference track, can also be plotted along with the Cloudsat cloud profiling data. Center swath pixels for the same parameters can also be shown as line plots overlaying the Cloudsat or Calipso profile images. Images and subsetted data produced in each analysis run may be downloaded. Users truly can explore and discover data specific to their needs prior to ever transferring data to their analysis tools.

Kempler, S.↗
NASA NTRS2003

HEASARC Software Archive

(1) Chandra Archive: SAO has maintained the interfaces through which HEASARC gains access to the Chandra Data Archive. At HEASARC's request, we have implemented an anonymous ftp copy of a major part of the public archive and we keep that archive up-to- date. SAO has participated in the ADEC interoperability working group, establishing guidelines or interoperability standards and prototyping such interfaces. We have provided an NVO-based prototype interface, intending to serve the HEASARC-led NVO demo project. HEASARC's Astrobrowse interface was maintained and updated. In addition, we have participated in design discussions surrounding HEASARC's Caldb project. We have attended the HEASARC Users Group meeting and presented CDA status and developments. (2) Chandra CALDB: SA0 has maintained and expanded the Chandra CALDB by including four new data file types, defining the corresponding CALDB keyword/identification structures. We have provided CALDB upgrades for the public (CIAO) and for Standard Data Processing. Approximately 40 new files have been added to the CALDB in these version releases. There have been in the past year ten of these CALDB upgrades, each with unique index configurations. In addition, with the inputs from software, archive, and calibration scientists, as well as CIAO/SDP software developers, we have defined a generalized expansion of the existing CALDB interface and indexing structure. The purpose of this is to make the CALDB more generally applicable and useful in new and future missions that will be supported archivally by HEASARC. The generalized interface will identify additional configurational keywords and permit more extensive calibration parameter and boundary condition specifications for unique file selection. HEASARC scientists and developers from SAO and GSFC have become involved in this work, which is expected to produce a new interface for general use within the current year. (3) DS9: One of the decisions that came from last year's HEADCC meeting was to make the ds9 image display program the primary vehicle for displaying line graphics (as well as images). The first step required to make this possible was to enhance the line graphics capabilities of ds9. SAO therefore spent considerable effort upgrading ds9 to use Tcl 8.4 so that the BLT line graphics package could be built and imported into ds9 from source code, rather than from a pre-built (and generally outdated) shared library. This task, which is nearly complete, allows us to extend BLT as needed for the HEAD community. Following HEADCC discussion concerning archiving and the display of archived data, we extended ds9 to support full access to many astronomical Web-based archives sites, including HEASARC, MAST, CHANDRA, SKYVIEW, ADS, NED, SIMBAD, IRAS, NVRO, SAO TDC, and FIRST. Using ds9's new internal Web access capabilities, these archives can be accessed via their Web page. FITS images, plots, spectra, and journal abstracts can be referenced, down-loaded, and displayed directly and easily in ds9. For more information, see: http://hea-www.harvard.edu/saord/ds9. Also after the HEADCC discussion concerning region filtering, we extended the Funtools sample implementation of region filtering as described in: http://hea-www.harvard.edu/saord/funtools/regions.html. In particular, we added several new composite regions for event and image filtering, including elliptical and box annuli. We also extended the panda (Pie AND Annulus) region support to include box pandas and elliptical pandas. These new composite regions are especially useful in programs that need to count photons in each separate region using only a single pass through the data. Support for these new regions was added to ds9. In the same vein, we developed new region support for filtering images using simple FITS image masks, i.e. 8-bit or 16-bit FITS images where the value of a pixel is the region id number for that pixel. Other important enhancements to DS9 this year, include supporor multiple world coordinate systems, three dimensional event file binning, image smoothing, region groups and tags, the ability to save images in a number of image formats (such as JPEG, TIFF, PNG, FITS), improvements in support for integrating external analysis tools, and support for the virtual observatory. In particular, a full-featured web browser has been implemented within D S 9 . This provides support for full access to HEASARC archive sites such as SKYVIEW and W3BROWSE, in addition to other astronomical archives sites such as MAST, CHANDRA, ADS, NED, SIMBAD, IRAS, NVRO, SA0 TDC, and FIRST. From within DS9, the archives can be searched, and FITS images, plots, spectra, and journal abstracts can be referenced, downloaded and displayed The web browser provides the basis for the built-in help facility. All DS9 documentation, including the reference manual, FAQ, Know Features, and contact information is now available to the user without the need for external display applications. New versions of DS9 maybe downloaded and installed using this facility. Two important features used in the analysis of high energy astronomical data have been implemented in the past year. The first is support for binning photon event data in three dimensions. By binning the third dimension in time or energy, users are easily able to detect variable x-ray sources and identify other physical properties of their data. Second, a number of fast smoothing algorithms have been implemented in DS9, which allow users to smooth their data in real time. Algorithms for boxcar, tophat, and gaussian smoothing are supported.

White, Nicholas↗