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At least 811 records · Page 45

Polar Coding For Forward Error Correction In Space Communications With LDPC Comparisons

With the surging development of optical telecommunicationsfor space applications, the importance of errorcorrection has become more apparent than ever. Specifically,the exploration of forward error correction code (FEC) methodologieswill be instrumental in developing the standards foroptical communications in space. Despite the widespread useof low-density parity-check (LDPC) codes, alternate FEC codessuch as polar codes have shown immense promise in assistingspace communications error correction with their ability tobypass the error floors that plague LDPC codes. Extremelypromising techniques including cyclic redundancy checks (CRC),successive cancellation (SC), and successive cancellation lists(SCL) that assist polar coding in achieving the Shannon limitin a timely manner are evaluated. MATLAB simulations areconducted with AWGN and burst noise to test each technique'sability to handle noise typically encountered in space and eachtechnique's ability to correct unexpected errors. Results ofsimulations for different rates and message lengths are alsoreported to determine each technique's ability to handle largedata volumes and fix errors. Similar simulations are conductedfor LDPC codes with additional tests for convolutional and nointerleavers. Finally, a discussion regarding the future ability ofpolar codes to satisfy current missions in the place of, or inconjunction with, LDPC codes along with the merits of eachFEC technique's ability to process data efficiently and handledata while maintaining adequate performance will be provided.Preliminary recommendations will be made for each technique'seffectiveness for GEO related missions along with discussionsregarding each technique's ability to fit within the CCSDS standards for optical communications.

Polar Coding↗

Python & Qt, Powerful Tools for Technical Computing

The objective of this presentation is to give a brief overview of Python computer language and Qt for Python which provides an interface to Python for building graphical applications. The Qt language provides a method for rapid programming of Graphical User Interfaces (GUIs) that are highly scalable, robust and platform independent. Both Python and Qt provide a powerful set of tools for Dynamic Analysis which are based on Open-Source software. Many problems and calculations in Structural Dynamics such as Power Spectral Density, Shock Response Spectrum and Vibration Response Spectrum can be easily calculated using these tools. The advantage of using Open-Source software is the ability to create custom graphical user interfaces similar to Matlab without the expense of software licensing and the ability to customize the software to an organization's specific needs. Also, another advantage is the ability to know which algorithms are being used by the GUI, know the numerical limitations and scale to large size data sets. I will end the presentation by demonstrating a Structural Dynamics GUI I created that was designed primarily to interactively analyze Post Flight high speed data provided by the ground station telemetry networks.

Grillo, Vincent↗

Optimal Control Prediction Method for Control Allocation

This paper proposes a novel prediction method for online optimal control allocation that extends the volume of moments achievable with the Moore-Penrose generalized inverse to the entire Attainable Moment Set. This method formulates the control allocation problem using selected basis vectors and associated gains which reduces the optimization problem dimensions and provides physical insight into the resulting optimal solutions. The proposed algorithm finds the entire family of unique optimal control solutions along the desired moment vector from the origin to the boundary of the Attainable Moment Set. Numerical results for the Moore-Penrose prediction method show that the unique minimal controls obtained yield the desired moment with near machine precision accuracy while maintaining control effectors within specified position limits. This method has been fully validated against the unique solution obtained on the boundary of the Attainable Moment Set using the Durham Direct Allocation method. Minimal control solutions obtained for moments in the interior of the Attainable Moment Set, similarly yield the desired moment to near machine precision while providing control solutions that are smaller (i.e. 2-norm) than solutions found with traditional control allocation algorithms (e.g. interior point methods) applied to the minimal control problem. Numerical simulations using a Matlab® autocoded executable (MEX) for the representative real world problem of 3-moments with 20 individual control effectors and prescribed control position limits show a mean computation speed of approximately 125 Hz which is sufficient to enable real-time flight allocation.

Acheson, Michael J.↗

Exit Presentation - Jared Ruzicka

The exit presentation provides an in depth examination of Spring 2020 NIFS intern, Jared Ruzicka’s, work on POST2 including creation of a module containing heritage aerodatabases and manual automation. The aerodatabase module incorporates a variety of legacy fortran and .dat aerodatabases into a POST2 module with example inputdecks verified by MATLAB mex files for 3 and 6 DOF simulations in nominal and dispersed conditions. The manual automation project discusses the transfer of the POST2 User’s Manual from word documents to text-based markdown files and the process through which a python script converts the manual to a PDF with improved formatting and compliance potential in a fraction of current manual generation time.

Jared Ruzicka↗

Permanent Magnetic Synchronous Motor (PMSM) Model Development

The Advanced Air Transport Technology (AATT) Project seeks to enhance the capabilities of fixed-wing subsonic transport through improved energy efficiency and environmental compatibility. One element of this effort is the use of high efficiency PMSM systems. Understanding the behavior and limitations of the se motors allows the National Aeronautics and Space Administration (NASA) to collaborate with and inform its partners worldwide. PMSM modeling is achieved using MATLAB®-Simulink® (The Math Works, Inc., Natick, Massachusetts). Through the analysis of an existing high-fidelity NASA Electrical Aircraft Testbed (NEAT) model, simplified models of varying fidelity can be developed with the goal of building a PMSM model capable of running in real-time for use in a piloted simulation environment. By utilizing system identification methods, it has been demonstrated that internal electrical components like the inverter can be represented by a simple variable gain thus greatly reducing the required simulation step size and subsequently decreasing model run-time by multiple orders of magnitude

motor controls↗

Autonomous Lunar PNT Simulator (ALPS)

In this paper we present a simulation tool that has been used to develop an architecture for a low-cost Position, Navigation and Time (PNT) system to provide PNT services on and around the Moon using non-dedicated low-cost orbital and ground assets. The simulation tool has been developed to be flexible and is capable of modeling and analyzing the many different capabilities and configurations that the non-dedicated assets could support. The tool models the creation of an ad hoc swarm, the localization of this swarm and the subsequent provision of PNT services from this swarm. We present results from several studies of select configurations chosen to reflect existing and future real-world needs and capabilities. Over the next few decades there is expected to be a substantial increase in Lunar missions supporting and inspired by NASA’s Artemis Program. It is expected that a large fraction of these missions will be low cost, utilizing rideshares and CubeSats, just as has seen in Earth orbit over the past decade. Many of these missions will need navigation capabilities but may be unable to support the large power, mass and weight that a weak GNSS or DSN based navigation solution would entail. As Lunar PNT service demand is not likely to be needed over the entire lunar surface 24/7, the creation of a dedicated Lunar GNSS constellation cannot be justified. Hence, our proposed architecture envisions utilizing existing Lunar science and exploration assets to create ad hoc and on demand Lunar PNT swarms capable of providing PNT services to low cost missions. The simulation reflects the two distinct parts of the architecture: the creation of a Lunar PNT swarm using non-dedicated existing assets, and the quantitative modeling of the quality of the PNT services that this swarm provides. To support the former, the simulation supports various swarm localization techniques, including centralized and distributed EKFs both of which support pluggable dynamics models. In modeling the PNT service performance the simulation adopts standard techniques from the GNSS community, including providing degree of precision (DOP) estimates for theoretical end users. Crucially, all asset capabilities, including clock accuracy, independent location self-knowledge and timing measurement precision can be set independently for each asset, reflecting the key concept of utilizing non-dedicated assets. The simulation is predominantly implemented in MATLAB, with GMAT being used for the propagation of orbital assets. The paper will present results from the simulation reflecting the tool’s flexibility and focusing on scenarios that match real-world proposed missions, including scenarios designed to provide PNT support to lunar surface missions similar to NASA Ames' forthcoming VIPER mission. The performance of centralized and decentralized swarm configuration and localization techniques will be compared. Finally, performance of the PNT service provided by an autonomous Lunar PNT swarm will be compared to existing radiometric and weak GNSS methods.

Kelley Elizabeth Hashemi↗

Modeling and Control for a Flexible Inverted Pendulum Robot

This report describes the tasks accomplished during Fall 2020 at the Kennedy Space Center under the scope of the Robotic Control System Design internship project. These tasks primarily supported development of an augmented adaptive control system for an inverted pendulum (IP) robotic system on a 4-wheel mobile base (Penny). This system serves as an analogue to the control problems in the flight of rockets in the initial and latter stages of launch, and methods developed as part of this research can later be applied to more complex IP systems, such as launch vehicles. To more accurately model launch vehicles, a flexible aluminum pendulum is used both on the hardware and in the simulated models. In order to capture the flexible dynamics of the system, hardware modifications were made to Penny (including installation of a rate gyro at the tip of the pendulum). Simulink models were created to control and model the hardware system, and Simscape models were created/updated to model the system in simulation. MATLAB programs were created throughout the internship to analyze data generated from hardware and simulation runs. Linear fixed-gain controllers have been applied to the simulated and hardware system, and work continues with augmenting these controllers using sensor blending and direct output adaptive control methods to improve stabilization of system states and cancel flex dynamics. In addition to describing the work done to support these modeling efforts, an Independent Research and Technology Development (IR&TD) proposal for a lunar simulation with soil deformation modeling developed during the internship is briefly described.

Nashir A Janmohamed↗

Developing a Multi-Lingual Autocoding Interface for the MAVERIC-II Dynamics Simulator

Simulation model development in certain high-level languages such as Python, MATLAB, or Simulink are unparalleled by their convenience and rapid turnover time. However, legacy simulation engines often depend on more traditional languages such as FORTRAN or C/C++. The NASA Marshall Aerospace Vehicle Representation in C version II (MAVERIC-II) is a modular, legacy-derived computer program used for high-fidelity, 6 degree-of-freedom (6dof) simulation for aerospace vehicle flights and analyses of guidance and control performance with built-in mathematical modeling of environmental effects such as wind, atmosphere, and gravity as well as dispersion capability for Monte Carlo analysis. MAVERIC-II is modular in the sense that each component software element of the simulation engine may be supplanted for a higher or lower fidelity version. The design flow of the development of these models is often performed in high-level languages as mentioned previously, which must then be translated into C or C++ code to be integrated into MAVERIC-II. We propose a unified method of autocoding and interfacing between several languages and MAVERIC-II, which may be generalized further to any type of 6dof simulation engine.

Mason Nixon↗

Mars Exploration Rovers landing dispersion analysis

Landing dispersion estimates for the Mars Exploration Rover missions were key elements in the site targeting process and in the evaluation of landing risk. This paper addresses the process and results of the landing dispersion analyses performed for both Spirit and Opportunity. The several contributors to landing dispersions (navigation and atmospheric uncertainties, spacecraft modeling, winds, and margins) are discussed, as are the analysis tools used. The JPL MarsLS program, a MATLAB-based landing dispersion visualization and statistical analysis tool, was used to calculate the probability of landing within hazardous areas. By convolving this with the probability of landing within flight system limits (in-spec landing) for each hazard area, a single overall measure of landing risk was calculated for each landing ellipse. In-spec probability contours were also generated, allowing a more synoptic view of site risks, illustrating the sensitivity to changes in landing location, and quantifying the possible consequences of anomalies such as incomplete maneuvers. Data and products required to support these analyses are described, including the landing footprints calculated by NASA Langley's POST program and the JPL AEPL program, cartographically registered base maps and hazard maps, and flight system estimates of in-spec landing probabilities for each hazard terrain type. Various factors encountered during operations, including evolving navigation estimates and changing atmospheric models, are discussed and final landing points are compared with approach estimates.

Desai, Prasun N.↗

Testing and characterization of an IR CBIRD FPA

High performance IR cameras are in great demand for a variety of applications, including defense (e.g. night vision, missile detection) and space (e.g. imaging at IR wavelengths). IR focal plane arrays (FPAs) are an essential component of IR cameras. An FPA is a collection of detectors at the focal plane of an imaging device, where each detector can be thought of a pixel in the image it is detecting. The FPA in this study is based on Complimentary Barrier Infrared Detectors (CBIRDs). The CBIRD FPA was tested by imaging flat black body targets at three different temperatures, 20ºC, 25ºC, and 30ºC, with images captured using SEIR software. The important figures of merit for an IR FPA, Noise Equivalent Difference Temperature (NEDT), Noise Equivalent Irradiance (NEI), detectivity (D*), responsivity, and quantum Efficiency (QE), were extracted from the image data for each individual detector using a MATLAB program, as was the array Modulation Transfer Function MTF. The test results show a mean NEDT of 16.98mK and an MTF of about 0.34 at the Nyquist frequency. Overall, the array performs well in the MWIR range and takes quality IR images.

Das, Tanya↗

WFIRST coronagraph optical modeling

End-to-end numerical optical modeling of the WFIRST coronagraph incorporating wavefront sensing and control is used to determine the performance of the coronagraph with realistic errors, including pointing jitter and polarization. We present the performance estimates of the current flight designs as predicted by modeling. We also describe the release of a new version of the PROPER optical propagation library, our primary modeling tool, which is now available for Python and Matlab in addition to IDL.

Zhou, Hanying↗

Fast Linearized Coronagraph Optimizer (FALCO) I: A Software Toolbox for Rapid Coronagraphic Design and Wavefront Correction

The Fast Linearized Coronagraph Optimizer (FALCO) is an open-source toolbox of routines for coronagraphic focal plane wavefront correction. The goal of FALCO is to provide a free, modular framework for the simulation or testbed operation of several common types of coronagraphs. FALCO includes routines for pair-wise probing estimation of the complex electric field and Electric Field Conjugation (EFC) control, and we ask the community to contribute other wavefront correction algorithms. FALCO utilizes and builds upon PROPER, an established optical propagation library. The key innovation in FALCO is the rapid computation of the linearized response matrix for each deformable mirror (DM), which facilitates re-linearization after each control step for faster DM-integrated coronagraph design and wavefront correction experiments. FALCO is freely available as source code in MATLAB at github.com/ajeldorado/falco-matlab and will be available later this year in Python 3 at github.com/ajeldorado/falco-python.

Shaklan, Stuart B.↗

Streamlining GNC Architecture Development and FSW Integration forthe Mars Ascent Vehicle

The Mars Ascent Vehicle (MAV) will be the first vehicle to perform an ascent from the surface ofanother atmospheric planetary body outside of the Earth-Moon system. Significant light-time delayrequires complete autonomy of flight throughout ascent, and naturally a high level of reliability isdesired in both MAV’s hardware and software subsystems. The MAV Guidance, Navigation and Controls(GNC) team and the MAV Flight Software (FSW) team have partnered together to improve the efficiencyof algorithm integration onto the MAV flight processor, and to increase confidence that said integrationis successful and without human error. An interface architecture is proposed for the GNC suite thatallows both the guidance and navigation subsystems to provide code algorithms directly in C++, and thecontrols subsystem to provide MATLAB Simulink auto-coded algorithms. Several continuous integration/deployment (CI/CD) methodologies have been considered for ease of transition of algorithm code fromthe GNC team to the FSW team. The GNC/FSW teams also worked together to develop a cFS-friendlywrapper which abstracts the integration of the GNC algorithm code into an interface-level API that iscompatible with cFS. Several iterations of vehicle GNC code have been produced between the GNC/FSWteam’s partnership, and this strong interface between these two teams have allowed the GNC/FSWteams to greatly increase confidence of efficient and error-free implementation of the GNC code ontoMAV for a successful flight.

GNC↗

Julia Language 1.1 Ephemeris Reader and Gravitational Modeling Program for Solar System Bodies

This paper analyzes the advancements to the Julia Language 1.1 Ephemeris and Physical constants Reader including the addition of gravitational modeling. Originally written in MATLAB, this Julia Language program is intended to be used in for trajectory design. Written in an open-source coding language, this ephemeris reader can output the state of planetary bodies including asteroids as well as other constants such as gravitational parameters. Two primary methods were chosen to calculate the gravitational potentials which include polyhedral modeling and spherical harmonics.

Gray, Brennan↗

Streamlining GNC Architecture Development and FSW Integration for the Mars Ascent Vehicle

The Mars Ascent Vehicle (MAV) will be the first vehicle to perform an ascent from the surface of another atmospheric planetary body outside of the Earth-Moon system. Significant light-time delay requires complete autonomy of flight throughout ascent, and naturally a high level of reliability is desired in both MAV’s hardware and software subsystems. The MAV Guidance, Navigation and Controls (GNC) team and the MAV Flight Software (FSW) team have partnered together to improve the efficiency of algorithm integration onto the MAV flight processor, and to increase confidence that said integration is successful and without human error. An interface architecture is proposed for the GNC suite that allows both the guidance and navigation subsystems to provide code algorithms directly in C++, and the controls subsystem to provide MATLAB Simulink auto-coded algorithms. Several continuous integration/deployment (CI/CD) methodologies have been considered for ease of transition of algorithm code from the GNC team to the FSW team. The GNC/FSW teams also worked together to develop a cFS-friendly wrapper which abstracts the integration of the GNC algorithm code into an interface-level API that is compatible with cFS. Several iterations of vehicle GNC code have been produced between the GNC/FSW team’s partnership, and this strong interface between these two teams have allowed the GNC/FSW teams to greatly increase confidence of efficient and error-free implementation of the GNC code onto MAV for a successful flight.

Engineering↗

Developing a Multilingual Auto-coding Interface Control for the MAVERIC-II Dynamics Simulator

Simulation model development in certain high-level languages such as Python, MATLAB, or Simulink are unparalleled by their convenience and rapid turnover time. However, legacy simulation engines often depend on more traditional languages such as FORTRAN or C/C++. The NASA Marshall Aerospace Vehicle Representation in C version II (MAVERIC-II) is a modular, legacy-derived computer program used for high-fidelity, 6 degree-of-freedom (6DOF) simulation for aerospace vehicle flights and analyses of guidance and control performance with built-in mathematical modeling of environmental effects such as wind, atmosphere, and gravity as well as dispersion capability for Monte Carlo analysis. MAVERIC-II is modular in the sense that each component software element of the simulation engine may be supplanted for a higher or lower fidelity version. The design flow of the development of these models is often performed in high-level languages as mentioned previously, which must then be translated into C or C++ code to be integrated into MAVERIC-II. Using principles of model-based design, we propose a unified method of auto-coding and interfacing between several languages and MAVERIC-II, which may be generalized further to any type of 6DOF simulation engine.

Mason Nixon↗

Scheduling Position, Navigation and Time Service Requests from Non-dedicated Lunar Constellations

This paper presents a centralized scheduler that satisfies user requests for Position, Navigation, and Time (PNT) services from an ad-hoc, non-dedicated orbital constellation around the Moon. Traditional, dedicated GNSS networks provide service 24/7, which allows users to acquire localization services at-will. For ad-hoc networks, a coordinated schedule is needed to ensure Quality of Service (QoS) guarantees for user localization, while satisfying non-dedicated assets’ usage constraints. This scheduler bridges this coordination gap by leveraging Mixed Integer-Linear Programming (MILP) to schedule this “as-needed” localization service while respecting the constraints on each asset. In upcoming decades there is expected to be a substantial increase in Lunar missions. Many of these missions will feature low-cost surface assets near the moon’s polar regions and small-sat science missions in orbit. Most missions need PNT capabilities to ensure safe operations and meet their science objectives, but low-cost missions may not be able to support the large power, mass, and weight that a weak GNSS or DSN based navigation solution would entail. Asset localization has been demonstrated using a decentralized extended Kalman Filter (DEKF) in the previously presented Lunar Autonomous PNT System (LAPS). Within the LAPS simulation environment, a module has been developed to generate the coordinated user-asset schedules described above; this Service Scheduler Module (SSM) allows for complete end-to-end testing of the entire system. Within SSM, a user service request consists of a location on the Lunar surface, a cumulative service duration, and a window in which service must occur. SSM takes as input these requests and the LAPS-predicted positional degree of precision as the QoS for each available set of orbital assets. A simple, baseline MILP model is formulated to provide the highest-precision service balanced across all requests. To reflect the non-dedicated nature of the constellation, this baseline model is augmented with additional asset-specific load capacity constraints or availability constraints. The load capacity constraints limit total time spent providing service, and the availability constraints reflect blockout times or availability windows when the assets are not otherwise occupied. SSM outputs two schedules: the user schedule to indicate their service times and expected QoS, and a satellite schedule to be transmitted to the orbiting constellation, describing when each non-dedicated asset provides PNT service. SSM is predominantly implemented in MATLAB and allows the use of any MILP solver to generate the resulting schedules. This paper describes the SSM - LAPS interface, how the output of LAPS is used to construct the MILP, and how SSM provides user localization service while satisfying constraints. It will also demonstrate the tool’s flexibility for formulating schedules for the end user and the constellation, focusing on scenarios that match real-world proposed missions. It will detail how SSM can be used to compare the addition of load capacity constraints, satellite availability constraints, and QoS guarantees for the users. Finally, we describe how SSM can be used to support the design of the ad-hoc constellation itself. The resulting integrated capability will support the design of future ad-hoc Lunar PNT networks, enabling high-quality, low-cost Lunar exploration

Swarm↗

Powder River Basin Water Resources: Mapping Russian Olive in the Powder River Basin to Inform Invasive Species Management

Since its introduction in the late 1800s, Elaeagnus augustifolia (Russian olive) has become a widespread invasive shrub that poses a threat to native riparian species in the United States by competing with native riparian plants for space and resources. To date, limited information on the distribution of Russian olive in the Powder River Basin of Montana and Wyoming have hampered management efforts and decision making. Here, we detect and model the distribution of Russian Olive using field surveys, ocular sampling, and variables from Landsat 8 Operational Land Imager (OLI), Sentinel-2 MultiSpectral Instrument (MSI), and Shuttle Radar Topography Mission (SRTM) using the Random Forest algorithm. We derived topographic, spectral, and hydrological variables from Landsat 8 OLI, Sentinel-2 MSI, and SRTM to utilize as model inputs. The team was able to successfully create a spectral Russian olive detection map for the Powder River Basin (RMSE =15.44%, R2 = 0.6482). The team also examined change in stream channel geomorphology from 1984-2020 in a time-series analysis using Landsat visible imagery and the RivMap MATLAB package and found little change. Our results will help our partners at the Powder River County Weed Board, Gay Ranch, United States Geological Survey, and University of Northern Colorado to locate and prioritize areas for riparian habitat restoration and to understand the region’s hydrology and geomorphology.

Catherine Buczek↗