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NASA NTRS2018

A Singular Value Decomposition Framework for Retrievals with Vertical Distribution Information from Greenhouse Gas Column Absorption Spectroscopy Measurements

We describe a variation of the Optimal Estimation (OE) method for greenhouse gas remote sensing retrievals using a singular value decomposition (SVD) and an uninformative prior. The SVD method is capable of discerning vertical information in column integrated absorption measurements. While traditional Bayesian optimal estimation (OE) assumes a prior distribution in order to regularize the inversion problem, the SVD approach identifies principal components that can be retrieved from the measurement without explicitly specifying a prior mean and prior covariance matrix. We discuss the method, illustrate its use on an integrated path differential absorption CO2 lidar measurement model, and compare it to traditional optimal estimation using numerical simulations. In the absence of forward model error, the mathematics behind the SVD method guarantee it to be bias-free, which is confirmed by the numerical simulations. In contrast, traditional OE retrievals exhibit bias when the prior mean used in the retrieval differs from the true mean. While the SVD approach can be used for most trace gas retrievals, it is particularly useful for situations where prior knowledge of the trace gas profile is poor. The SVD analysis is also useful in illustrating how vertical information is treated by the traditional OE approach.

Ramanathan, Anand K.↗

NASA NTRS2017

Predictive Modeling of Fast-Curing Thermosets in Nozzle-Based Extrusion

This work presents an approach to modeling the dynamic spreading and curing behavior of thermosets in nozzle-based extrusions. Thermosets cover a wide range of materials, some of which permit low-temperature processing with subsequent high-temperature and high-strength working properties. Extruding thermosets may overcome the limited working temperatures and strengths of conventional thermoplastic materials used in additive manufacturing. This project aims to produce technology for the fabrication of thermoset-based structures leveraging advances made in nozzle-based extrusion, such as fused deposition modeling (FDM), material jetting, and direct writing. Understanding the synergistic interactions between spreading and fast curing of extruded thermosetting materials will provide essential insights for applications that require accurate dimensional controls, such as additive manufacturing [1], [2] and centrifugal coating/forming [3]. Two types of thermally curing thermosets -- one being a soft silicone (Ecoflex 0050) and the other being a toughened epoxy (G/Flex) -- served as the test materials in this work to obtain models for cure kinetics and viscosity. The developed models align with extensive measurements made with differential scanning calorimetry (DSC) and rheology. DSC monitors the change in the heat of reaction, which reflects the rate and degree of cure at different crosslinking stages. Rheology measures the change in complex viscosity, shear moduli, yield stress, and other properties dictated by chemical composition. By combining DSC and rheological measurements, it is possible to establish a set of models profiling the cure kinetics and chemorheology without prior knowledge of chemical composition, which is usually necessary for sophisticated mechanistic modeling. In this work, we conducted both isothermal and dynamic measurements with both DSC and rheology. With the developed models, numerical simulations yielded predictions of diameter and height of droplets, along with width and height of extruded lines cured at varied temperatures. Experimental results carried out on a goniometric platform and a nozzle-based 3D printer showed agreement with the numerical simulations. Finally, this presentation will show how the models are adaptable to the planning of tool paths and designs in additive manufacturing.

Xie, Jingjin↗

NASA NTRS2017

Network Analysis of Rodent Transcriptomes in Spaceflight

Network analysis methods leverage prior knowledge of cellular systems and the statistical and conceptual relationships between analyte measurements to determine gene connectivity. Correlation and conditional metrics are used to infer a network topology and provide a systems-level context for cellular responses. Integration across multiple experimental conditions and omics domains can reveal the regulatory mechanisms that underlie gene expression. GeneLab has assembled rich multi-omic (transcriptomics, proteomics, epigenomics, and epitranscriptomics) datasets for multiple murine tissues from the Rodent Research 1 (RR-1) experiment. RR-1 assesses the impact of 37 days of spaceflight on gene expression across a variety of tissue types, such as adrenal glands, quadriceps, gastrocnemius, tibalius anterior, extensor digitorum longus, soleus, eye, and kidney. Network analysis is particularly useful for RR-1 -omics datasets because it reinforces subtle relationships that may be overlooked in isolated analyses and subdues confounding factors. Our objective is to use network analysis to determine potential target nodes for therapeutic intervention and identify similarities with existing disease models. Multiple network algorithms are used for a higher confidence consensus.

genomics↗

NASA NTRS2017

Minimizing Human Risk: Human Performance Models in the Human Factors and Behavioral Performance Element

Human space exploration has never been more exciting than it is today. Human presence to outer worlds is becoming a reality as humans are leveraging much of our prior knowledge to the new mission of going to Mars. Exploring the solar system at greater distances from Earth than ever before will possess some unique challenges, which can be overcome thanks to the advances in modeling and simulation technologies. The National Aeronautics and Space Administration (NASA) is at the forefront of exploring our solar system. NASA's Human Research Program (HRP) focuses on discovering the best methods and technologies that support safe and productive human space travel in the extreme and harsh space environment. HRP uses various methods and approaches to answer questions about the impact of long duration missions on the human in space including: gravitys impact on the human body, isolation and confinement on the human, hostile environments impact on the human, space radiation, and how the distance is likely to impact the human. Predictive models are included in the HRP research portfolio as these models provide valuable insights into human-system operations. This paper will provide an overview of NASA's HRP and will present a number of projects that have used modeling and simulation to provide insights into human-system issues (e.g. automation, habitat design, schedules) in anticipation of space exploration.

human systems integrations↗

NASA NTRS2018

A Fault-Tolerant Clock Synchronization and Geometry Determination Protocol

A fault-tolerant distributed protocol (algorithm) is presented that achieves optimum timing precision (clock synchronization) among the nodes and, simultaneously, determines the network's geometry (shape) - locations and distances of the nodes relative to each other - in a wireless distributed system. This protocol is based on the assumption of initial coarse synchrony of nodes' local clocks. The proposed solution assumes no prior knowledge of the nodes' locations, the distances between the nodes, or network's geometry, but assumes an ordered geometry where nodes have unique identifiers. This protocol accommodates large variations in the communication latencies among the nodes; thus, it applies equally to both wireless and wired networks.

Malekpour, Mahyar R.↗

NASA NTRS2018

Slides for Focus Group-UTM

These slides form the first talking points for the CON3 focus groups. They are designed to give an overview of the UTM project to people with no prior knowledge so they can help us with ideas for a public portal USS.

UTM↗

NASA NTRS2017

Online Multi-Modal Learning and Adaptive Information Trajectory Planning for Autonomous Exploration

In robotic information gathering missions, scientists are typically interested in understanding variables which require proxy measurements from specialized sensor suites to estimate. However, energy and time constraints limit how often these sensors can be used in a mission. Robots are also equipped with cheaper to use navigation sensors such as cameras. In this paper, we explore a challenging planning problem in which a robot is required to learn about a scientific variable of interest in an initially unknown environment by planning informative paths and deciding when and where to use its sensors. To tackle this we present two innovations: a Bayesian generative model framework to automatically learn correlations between expensive science sensors and cheaper to use navigation sensors online, and a sampling based approach to plan for multiple sensors while handling long horizons and budget constraints. Our approach does not grow in complexity with data and is anytime making it highly applicable to field robotics. We tested our approach extensively in simulation and validated it with real data collected during the 2014 Mojave Volatiles Prospector Mission. Our planning algorithm performs statistically significantly better than myopic approaches and at least as well as a coverage-based algorithm in an initially unknown environment while having added advantages of being able to exploit prior knowledge and handle other intricacies of the real world without further algorithmic modifications.

learning↗

NASA NTRS2018

Comparison of Magnetic Fields from a New Satellite Magnetic Model of the Lithosphere with Magnetic Fields Predicted from Crust 1.0

Both magnetic and seismic techniques can provide information about the Moho (Mohorovicic discontinuity). We develop a new technique that provides a better estimate of the magnetic thickness of the crust, as compared with previous approaches. It uses prior knowledge from seismology (Crust 1.0), a new high-degree model from CHAMP (CHAllenging Mini-satellite Payload) and Swarm (LCS-1 - a model of Earth's lithospheric field) and a newly developed technique. The technique is appropriate for regions where induced magnetization dominates over remanent magnetization. We compare the predictions from LCS-1 with those from Crust 1.0, with some simple assumptions, and find that the correlations increase until about spherical harmonic degree 30, and then decrease globally. Spatially, the correlations between the seismic and magnetic techniques are strongest over North America and Australia, and weakest over South America and northern Africa. Strong correlations also exist between the two approaches over the Antarctic, northern Europe, and Greenland. While we might expect the seismic and magnetic approaches to correlate over well-characterized regions (i.e. North America), and show weaker correlations over poorly-characterized regions (i.e. South America and north Africa), the strong correlation in the Antarctic and Greenland is puzzling, because both of these regions are poorly-characterized. We discuss some possible explanations, and implications, of this attempt to correlate seismic and magnetic approaches to characterizing the lithosphere.

Seismic↗

NASA NTRS2019

A Communication Channel Density Estimating Generative Adversarial Network

Autoencoder-based communication systems use neural network channel models to backwardly propagate message reconstruction error gradients across an approximation of the physical communication channel. In this work, we develop and test a new generative adversarial network (GAN) architecture for the purpose of training a stochastic channel approximating neural network. In previous research, investigators have focused on additive white Gaussian noise (AWGN) channels and/or simplified Rayleigh fading channels, both of which are linear and have well defined analytic solutions. Given that training a neural network is computationally expensive, channel approximation networks— and more generally the autoencoder systems—should be evaluated in communication environments that are traditionally difficult. To that end, our investigation focuses on channels that contain a combination of non-linear amplifier distortion, pulse shape filtering, intersymbol interference, frequency-dependent group delay, multipath, and non-Gaussian statistics. Each of our models are trained without any prior knowledge of the channel. We show that the trained models have learned to generalize over an arbitrary amplifier drive level and constellation alphabet. We demonstrate the versatility of our GAN architecture by comparing the marginal probability density function of several channel simulations with that of their corresponding neural network approximations

Smith, Aaron↗

NASA NTRS2019

The Cumulus and Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)

Low clouds continue to contribute greatly to the uncertainty in cloud feedback estimates. Depending on whether a region is dominated by cumulus (Cu) or stratocumulus (Sc) clouds, the interannual low-cloud feedback is somewhat different in both spaceborne and large-eddy simulation studies. Therefore, simulating the correct amount and variation of the Cu and Sc cloud distributions could be crucial to predict future cloud feedbacks. Here we document spatial distributions and profiles of Sc and Cu clouds derived from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat measurements. For this purpose, we create a new dataset called the Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD), which identifies Sc, broken Sc, Cu under Sc, Cu with stratiform outflow and Cu. To separate the Cu from Sc, we design an original method based on the cloud height, horizontal extent, vertical variability and horizontal continuity, which is separately applied to both CALIPSO and combined CloudSat–CALIPSO observations. First, the choice of parameters used in the discrimination algorithm is investigated and validated in selected Cu, Sc and Sc–Cu transition case studies. Then, the global statistics are compared against those from existing passive- and active-sensor satellite observations. Our results indicate that the cloud optical thickness – as used in passive-sensor observations – is not a sufficient parameter to discriminate Cu from Sc clouds, in agreement with previous literature. Using clustering-derived datasets shows better results although one cannot completely separate cloud types with such an approach. On the contrary, classifying Cu and Sc clouds and the transition between them based on their geometrical shape and spatial heterogeneity leads to spatial distributions consistent with prior knowledge of these clouds, from ground-based, ship-based and field campaigns. Furthermore, we show that our method improves existing Sc–Cu classifications by using additional information on cloud height and vertical cloud fraction variation. Finally, the CASCCAD datasets provide a basis to evaluate shallow convection and stratocumulus clouds on a global scale in climate models and potentially improve our understanding of low-level cloud feedbacks. The CASCCAD dataset (Cesana, 2019, https://doi.org/10.5281/zenodo.2667637) is available on the Goddard Institute for Space Studies (GISS) website at https://data.giss.nasa.gov/clouds/casccad/ (last access: 5 November 2019) and on the zenodo website at https://zenodo.org/record/2667637 (last access: 5 November 2019).

Cesana, Gregory V.↗

NASA NTRS2020

In-Time UAV Flight-Trajectory Estimation and Tracking Using Bayesian Filters

Rapid increase of UAV operation in the next decade in areas of on-demand delivery, medical transportation services, law enforcement, traffic surveillance and several others pose potential risks to the low altitude airspace above densely populated areas. Safety assessment of airspace demands the need for a novel UAV traffic management (UTM) framework for regulation and tracking of the vehicles. Particularly for low-altitude UAV operations, quality of GPS measurements feeding into the UAV is often compromised by loss of communication link caused by presence of trees or tall buildings in proximity to the UAV flight path. Inaccurate GPS locations may yield to unreliable monitoring and inaccurate prognosis of remaining battery life and other safety metrics which rely on future expected trajectory of the UAV. This work therefore proposes a generalized monitoring and prediction methodology for autonomous UAVs using in-time GPS measurements. Firstly, a typical 4D smooth trajectory generation technique from a series of waypoint locations with associated expected times-of-arrival based on B-spline curves is presented. Initial uncertainty in the vehicle's expected cruise velocity is quantified to compute confidence intervals along the entire flight trajectory using error interval propagation approach. Further, the generated planned trajectory is considered as the prior knowledge which is updated during its flight with incoming GPS measurements in order to estimate its current location and corresponding kinematic profiles. Estimation of position is denoted in dicrete state-space representation such that position at a future time step is derived from position and velocity at current time step and expected velocity at the future time step. A linear Bayesian filtering algorithm is employed to efficiently refine position estimation from noisy GPS measurements and update the confidence intervals. Further, a dynamic re-planning strategy is implemented to incorporate unexpected detour or delay scenarios. Finally, critical challenges related to uncertainty quantification in trajectory prognosis for autonomous vehicles are identified, and potential solutions are discussed at the end of the paper. The entire monitoring framework is demonstrated on real UAV flight experiments conducted at the NASA Langley Research Center.

Banerjee, Portia↗

NASA NTRSDate not supplied

Multi-Impulse to Time Optimal Finite Burn Trajectory Conversion

A novel algorithm is presented that provides an improvement over a traditional parameter optimization method when solving time-optimal, finite-burn pseudo-rendezvous spacecraft trajectory problems. A hybrid optimization procedure is described that converts a set of multiple-impulses, representing high- or low-thrust maneuvers, to an exact time-optimal finite-burn trajectory for a thrust limited, constant exhaust velocity spacecraft. The Hybrid Method applies a control law derived from the Euler-Lagrange system of equations within the classical Indirect Method to a modern Direct Method. An iterative adjoint-control transformation and an evolving constraint vector are introduced to solve the optimal control two-point boundary value problem. This method requires no prior knowledge of the solution, which adds simplicity to the trajectory design process and aids automation. Examples are shown for low-thrust apogee raise maneuvers, non-coplanar Earth orbit transfers, and a modified Deep Space 1 low-thrust trajectory. A numerically significant improvement to objective cost is shown across all application problems compared to a traditional solution method, as well as a significant improvement to convergence speed for a select class of problems.

Joshua A Fogel↗

NASA NTRSDate not supplied

Dude Where's My Stars: A Novel Topologically Justified Approach to Star Tracking

In this paper, we consider two novel approaches to celestial navigation for spacecraft. Determining attitude without any prior knowledge using star tracking is known to be a difficult task, particularly given the computational complexity and the many potential sources of misinformation. We consider localization by optimizing matching parameters without explicit star identification in a computationally tractable manner. This is achieved using the mathematical tools of topological data analysis (TDA) and cellular sheaves to study the geometry and distribution of cataloged stars. A framework is gained that enhances the statistical approach to noise handling and false star detection, and heterogeneous sensor fusion. Finally, we discuss confidence bounds and minimum information requirements for successful operation.

Sheaf theory↗

NASA NTRS2018

Assurance of Autonomy for Robotic Space Missions

While there have been meetings on assurance of autonomy in other application areas, this meeting seeks to develop a roadmap for assurance of autonomy specifically for robotic space missions. Key characteristics that distinguish this application area include: the lack of detailed prior knowledge of the environments in which those missions are to operate; the challenges of mimicking deep space operating conditions for purposes of testing; the need to be highly assured of failsafe operation; limited and delayed (due to speed of light over solar system distances) communication; and the one/few-of-a-kind, must-work-the-first-time nature of most space missions.

Feather, Martin↗

NASA NTRS2016

Smart Congestion Control for Delay- and Disruption Tolerant Networks

In this paper, we propose a novel congestion control framework for delay- and disruption tolerant networks (DTNs). The proposed framework, called Smart-DTN-CC, adjusts its operation automatically as a function of the dynamics of the underlying network. It employs reinforcement learning, a machine learning technique known to be well suited to problems in which the environment, in this case the network, plays a crucial role; yet, no prior knowledge about the target environment can be assumed, i.e., the only way to acquire information about the environment is to interact with it through continuous online learning. Smart-DTN-CC nodes get input from the environment (e.g., its buﬀer occupancy, set of neighbors, etc), and, based on that information, choose an action to take from a set of possible actions. Depending on an action’s eﬀectiveness in controlling congestion, it will be given a reward. Smart-DTN-CC’s goal is to maximize the overall reward which translates to minimizing congestion. To our knowledge, Smart-DTN-CC is the ﬁrst DTN congestion control framework that has the ability to automatically and continuously adapt to the dynamics of the target environment which allows Smart-DTNCC to deliver adequate performance in a variety of DTN applications and scenarios. As demonstrated by our experimental evaluation, Smart-DTN-CC is able to consistently outperform existing DTN congestion control mechanisms under a wide range of network conditions and characteristics.

Hirata, Celso M.↗

NASA NTRS2021

A Spectrum Sensor for CubeSat Radios

Cube satellite (CubeSat) launches have increased exponentially over the last 20 years. This class of miniature spacecraft is well-suited for a set of nonconventional satellite architectures collectively known as formation flying. With the exponential pace of launches expected to continue, the prospect of spectrum management for these complex formations arises. In previous work, investigators focus on terrestrial applications of spectrum sensing, which have the luxury to utilize hardware with high size, weight, and power (SWaP) resources. In this work, we develop and test a spectrum sensor for CubeSat radio applications. Given that CubeSat radios are inherently designed for low SWaP, they cannot implement the computationally expensive spectral correlation analyzer (SCA) algorithms for signal detection. To that end, our investigation focuses on the application of the SCA to square-root-raised-cosine (SRRC) pulse-shaped quadrature amplitude modulation (QAM) waveforms using a field-programmable gate array (FPGA). This model requires no prior knowledge of the radio-frequency (RF) channel. We show that this model can consistently and accurately detect the symbol rate and center frequencies of waveforms located in a spectrum.

cubesat↗

NASA NTRS2021

Model-Independent Time-Delay Interferometry Based on Principal Component Analysis

With a laser interferometric gravitational-wave detector in separate free flying spacecraft, the only way to achieve detection is to mitigate the dominant noise arising from the frequency fluctuations of the lasers via postprocessing. The noise can be effectively filtered out on the ground through a specific technique called time-delay interferometry (TDI), which relies on the measurements of time-delays between spacecraft and careful modeling of how laser noise enters the interferometric data. Recently, this technique has been recast into a matrix-based formalism by several authors, offering a different perspective on TDI, particularly by relating it to principal component analysis (PCA). In this work, we demonstrate that we can cancel laser frequency noise by directly applying PCA to a set of shifted data samples, without any prior knowledge of the relationship between single-link measurements and noise, nor time-delays. We show that this fully data-driven algorithm achieves a gravitational-wave sensitivity similar to classic TDI.

Quentin Baghi↗

NASA NTRS2021

Identifying Meteorological Influences on Marine Low Cloud Mesoscale Morphology Using Satellite Classifications

Marine low cloud mesoscale morphology in the southeastern Pacific Ocean is analyzed using a large dataset of machine-learning generated classifications spanning three years. Meteorological variables and cloud properties are composited 10by mesoscale cloud type, showing distinct meteorological regimes of marine low cloud organization from the tropics to the midlatitudes. The presentation of mesoscale cellular convection, with respect to geographic distribution, boundary layer structure, and large-scale environmental conditions, agrees with prior knowledge. Two tropical and subtropical cumuliform boundary layer regimes, suppressed cumulus and clustered cumulus, are studied in detail. The patterns in precipitation, circulation, column water vapor, and cloudiness are consistent with the representation of marine shallow mesoscale convective 15 self-aggregation by large eddy simulations of the boundary layer. Although they occur under similar large-scale conditions, the suppressed and clustered low cloud types are found to be well-separated by variables associated with low-level mesoscale circulation, with surface wind divergence being the clearest discriminator between them, whether reanalysis or satellite observations are used. Clustered regimes are associated with surface convergence and suppressed regimes are associated with surface divergence.

Johannes Mohrmann↗