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At least 541 records · Page 30

Micro-Spec: an Integrated, Direct-Detection Spectrometer for Far-Infrared and Submillimeter Astronomy

The far-infrared and submillimeter portions of the electromagnetic spectrum provide a unique view of the astrophysical processes present in the early universe. Our ability to fully explore this rich spectral region has been limited, however, by the size and cost of the cryogenic spectrometers required to carry out such measurements. Micro-Spec (u-Spec) is a high-sensitivity, direct-detection spectrometer concept working in the 450-1000 micromillimeter wavelength range which will enable a wide range of flight missions that would otherwise be challenging due to the large size of current instruments with the required spectral resolution and sensitivity. The spectrometer design utilizes two internal antenna arrays, one for transmitting and one for receiving, superconducting microstrip transmission lines for power division and phase delay, and an array of microwave kinetic inductance detectors (MKIDs) to achieve these goals. The instrument will be integrated on a approximately 10 square cm silicon chip and can therefore become an important capability under the low background conditions accessible via space and high-altitude borne platforms. In this paper, an optical design methodology for Micro-Spec is presented, with particular attention given to its twodimensional diffractive region, where the light of different wavelengths is focused on the different detectors. The method is based on the maximization of the instrument resolving power and minimization of the RMS phase error on the instrument focal plane. This two-step optimization can generate geometrical configurations given specific requirements on spectrometer size, operating spectral range and performance. A point design with resolving power of 257, an RMS phase error less than 0.1 radians and four stigmatic points was developed for initial demonstration and will be the basis of future instruments with resolving power up to about 1200.

microwave kinetic inductance detectors (MKIDs)↗

Micro-spec: an Integrated Direct-detection Spectrometer for Far-infrared Space Telescopes

The far-infrared and submillimeter portions of the electromagnetic spectrum provide a unique view of the astrophysical processes present in the early universe. Our ability to fully explore this rich spectral region has been limited, however, by the size and cost of the cryogenic spectrometers required to carry out such measurements.Micro-Spec (μ-Spec) is a high-sensitivity, direct-detection spectrometer concept working in the 450-1000 (micrometers) wavelength range which will enable a wide range of flight missions that would otherwise be challenging due tothe large size of current instruments with the required spectral resolution and sensitivity. The spectrometer design utilizes two internal antenna arrays, one for transmitting and one for receiving, superconducting microstrip transmission lines for power division and phase delay, and an array of microwave kinetic inductance detectors (MKIDs) to achieve these goals. The instrument will be integrated on a approximately 10 sq cm silicon chip and can therefore become an important capability under the low background conditions accessible via space and high-altitude borne platforms. In this paper, an optical design methodology for micro-Spec is presented, with particular attention given to its two-dimensional diffractive region, where the light of different wavelengths is focused on the different detectors. The method is based on the maximization of the instrument resolving power and minimization of the RMS phase error on the instrument focal plane. This two-step optimization can generate geometrical configurations given specific requirements on spectrometer size, operating spectral range and performance.Two point designs with resolving power of 260 and 520 and an RMS phase error less than approximately 0.004 radians were developed for initial demonstration and will be the basis of future instruments with resolving power up to about 1200.

Far-infrared↗

An Airborne Scanning Laser Altimetry Survey of Long Valley, California

Between 28 September and 7 October 1995, we conducted an airborne laser altimetry experiment over the Long Valley caldera, California, in which each of two scanning laser altimeters (dubbed SLICER and RASCAL) were flown in a NASA T-39 jet aircraft. Operating concurrently were a Global Positioning System (GPS) guidance system and dual frequency receivers for precise navigation and post-flight calculation or the airplane trajectory relative to a ground station, and an inertial navigation system (INS) for attitude determination. Reduction of raw laser ranges requires merging the differential kinematic GPS aircraft trajectory and the INS data with the laser data, and determination of the atmospheric delay. Data geolocation consists of obtaining the centre location and the mean elevation within each footprint in a geodetic coordinate system. The elevation of Crowley Lake is recovered to an accuracy of approximately 3 cm or better from 3 km above ground level and crossover analysis indicates that the elevation estimates are consistent from pass to pass. We test our geolocation procedures by comparing laser-derived elevations with those determined in situ for recognizable ground features. A comparison of laser and GPS-derived positions shows that the horizontal accuracy is better than the diameter of the footprint and vertical accuracy is within the error inherent in the range measurement. A comparison of SLICER elevation data with digital elevation models (DEMs) of the region shows that the DEM data provides surface topography to within stated accuracy limits. Although research continues to utilize the full potential of laser altimetry data, our results constitute a successful demonstration that the technique may be used to perform geodetic monitoring of surface topographic changes.

Hofton, M. A.↗

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

Soil Surface Runoff Scheme for Improving Land-Hydrology and Surface Fluxes in Simple SiB (SSiB)

Evapotranspiration on land is hard to measure and difficult to simulate. On the scale of a GCM grid, there is large subgrid-scale variability of orography, soil moisture, and vegetation. Our hope is to be able to tune the biophysical constants of vegetation and soil parameters to get the most realistic space-averaged diurnal cycle of evaporation and its climatology. Field experiments such as First ISLSCP Field Experiment (FIFE), Boreal Ecosystem-Atmosphere Study (BOREAS), and LBA help a great deal in improving our evapotranspiration schemes. However, these improvements have to be matched with, and coupled to, consistent improvement in land-hydrology; otherwise, the runoff problems will intrinsically reflect on the soil moisture and evapotranspiration errors. Indeed, a realistic runoff simulation also ensures a reasonable evapotranspiration simulation provided the precipitation forcing is reliable. We have been working on all of the above problems to improve the simulated hydrologic cycle. Through our participation in the evaluation and intercomparison of land-models under the behest of Global Soil Wetness Project (GSWP), we identified a few problems with Simple SiB (SSIB; Xue et al., 1991) hydrology in regions of significant snowmelt. Sud and Mocko (1999) show that inclusion of a separate snowpack model, with its own energy budget and fluxes with the atmosphere aloft and soil beneath, helps to ameliorate some of the deficiencies of delayed snowmelt and excessive spring season runoff. Thus, much more realistic timing of melt water generation was simulated with the new snowpack model in the subsequent GSWP re-evaluations using 2 years of ISLSCP Initiative I forcing data for 1987 and 1988. However, we noted an overcorrection of the low meltwater infiltration of SSiB. While the improvement in snowmelt timing was found everywhere, the snowmelt infiltration has became excessive in some regions, e.g., Lena river basin. This leads to much reduced runoff in many basins as compared to observations. We believe this is a consequence of neglect of the influence of subgrid-scale variations in orography that affects the production of surface runoff.

Sud, Y. C.↗

Evaluating LSM-Based Water Budgets Over a West African Basin Assisted with a River Routing Scheme

Within the framework of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project phase 2 (ALMIP-2), this study evaluates the water balance simulated by the Interactions between Soil, Biosphere, and Atmosphere (ISBA) over the upper Oum River basin, in Benin, using a mesoscale river routing scheme (RRS). The RRS is based on the nonlinear Muskingum Cunge method coupled with two linear reservoirs that simulate the time delay of both surface runoff and base flow that are produced by land surface models. On the basis of the evidence of a deep water-table recharge in that region,a reservoir representing the deep-water infiltration (DWI) is introduced. The hydrological processes of the basin are simulated for the 2005-08 AMMA field campaign period during which rainfall and stream flow data were intensively collected over the study area. Optimal RRS parameter sets were determined for three optimization experiments that were performed using daily stream flow at five gauges within the basin. Results demonstrate that the RRS simulates stream flow at all gauges with relative errors varying from -22% to 3% and Nash-Sutcliffe coefficients varying from 0.62 to 0.90. DWI varies from 24% to 67% of the base flow as a function of the sub-basin. The relatively simple reservoir DWI approach is quite robust, and further improvements would likely necessitate more complex solutions (e.g., considering seasonality and soil type in ISBA); thus, such modifications are recommended for future studies. Although the evaluation shows that the simulated stream flows are generally satisfactory, further field investigations are necessary to confirm some of the model assumptions.

Multidisciplinary↗

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↗

Improving TCP Network Performance by Detecting and Reacting to Packet Reordering

There are many factors governing the performance of TCP-basec applications traversing satellite channels. The end-to-end performance of TCP is known to be degraded by the reordering, delay, noise and asymmetry inherent in geosynchronous systems. This result has been largely based on experiments that evaluate the performance of TCP in single flow tests. While single flow tests are useful for deriving information on the theoretical behavior of TCP and allow for easy diagnosis of problems they do not represent a broad range of realistic situations and therefore cannot be used to authoritatively comment on performance issues. The experiments discussed in this report test TCP s performance in a more dynamic environment with competing traffic flows from hundreds of TCP connections running simultaneously across the satellite channel. Another aspect we investigate is TCP's reaction to bit errors on satellite channels. TCP interprets loss as a sign of network congestion. This causes TCP to reduce its transmission rate leading to reduced performance when loss is due to corruption. We allowed the bit error rate on our satellite channel to vary widely and tested the performance of TCP as a function of these bit error rates. Our results show that the average performance of TCP on satellite channels is good even under conditions of loss as high as bit error rates of 10(exp -5)

Kruse, Hans↗

Miniature Telerobots in Space Applications

Ground controlled telerobots can be used to reduce astronaut workload while retaining much of the human capabilities of planning, execution, and error recovery for specific tasks. Miniature robots can be used for delicate and time consuming tasks such as biological experiment servicing without incurring the significant mass and power penalties associated with larger robot systems. However, questions remain regarding the technical and economic effectiveness of such mini-telerobotic systems. This paper address some of these open issues and the details of two projects which will provide some of the needed answers. The Microtrex project is a joint University of Washington/NASA project which plans on flying a miniature robot as a Space Shuttle experiment to evaluate the effects of microgravity on ground-controlled manipulation while subject to variable time-delay communications. A related project involving the University of Washington and Boeing Defense and Space will evaluate the effectiveness f using a minirobot to service biological experiments in a space station experiment 'glove-box' rack mock-up, again while subject to realistic communications constraints.

Venema, S. C.↗

Predicted Performance of On-Off Systems for Precise Satellite Attitude Control

An investigation has been made of the use of on-off reaction jets for precision attitude control of a satellite. Since a symmetrical vehicle is assumed, only single-axis control needs to be considered. The responses to initial disturbances and also limit-cycle characteristics for several systems have been evaluated. Calculated results indicate that realistic values of settling time and fuel consumption for the example considered can be obtained. The performance of a given system depends on the characteristics of the error detector used. In cases where the detector output was saturated for a relatively low error input, the settling time deteriorated when a lead network was used to provide damping. This deterioration could be eliminated if a separate rate signal to produce vehicle rate limiting were available. As an alternate approach, two systems were investigated which used a timed sequence of torques and could operate with a detector output of very small linear range. Although the performance of these systems was poorer than that of the lead network system without detector saturation, the performance was better than that of the lead network system with low values of detector saturation. The effects on limit-cycle characteristics of hysteresis, lead network constants, dead zone, and thrust time delays were also investigated.

Brown, Stuart C.↗

Evaluation of Improved Pushback Forecasts Derived from Airline Ground Operations Data

Accurate and timely predictions of airline pushbacks can potentially lead to improved performance of automated decision-support tools for airport surface traffic, thus reducing the variability and average duration of costly airline delays. One factor which affects the realization of these benefits is the level of uncertainty inherent in the turn processes. To characterize this inherent uncertainty, three techniques are developed for predicting time-to-go until pushback as a function of available ground-time; elapsed ground-time; and the status (not-started/in-progress/completed) of individual turn processes (cleaning, fueling, etc.). These techniques are tested against a large and detailed dataset covering approximately l0(exp 4) real-world turn operations obtained through collaboration with Deutsche Lufthansa AG. Even after the dataset is filtered to obtain a sample of turn operations with minimal uncertainty, the standard deviation of forecast error for all three techniques is lower-bounded away from zero, indicating that turn operations have a significant stochastic component. This lower-bound result shows that decision-support tools must be designed to incorporate robust mechanisms for coping with pushback demand stochasticity, rather than treating the pushback demand process as a known deterministic input.

Carr, Francis↗

Assessing the Altimetric Measurement from CYGNSS Data

The Cyclone Global Navigation Satellite System (CYGNSS) mission was designed to study hurricane intensification by measuring wind speeds in tropical cyclones. However, the delay-Doppler maps (DDM) that are produced can be used to estimate the sea surface height (SSH) at the specular reflection point on the ocean surface. Proofof-concept studies that DDMs are suitable to solve for SSH have been recently reported (Clarizia et al., 2016; Mashburn et al., 2018), based on data acquired by the demonstration satellite experiment Tech Demo Sat - 1 (TDS-1) carrying a GNSS-R receiver similar to the ones onboard CYGNSS. Although the precision of each 1sec averaged SSH is considerably lower than that of the existing satellite altimeters, by virtue of the dense coverage and frequent revisit time exhibited by the constellation of 8 microsats, the error may be smoothed down considerably by optimal interpolation (Li et al., 2016). Hence the CYGNSS dataset presents a potential opportunity to sample the tropical oceans, and investigate the sensitivity of the SSH measurements to mesoscale eddies. Our objective is to analyze the CYGNSS data and test a suite of retrieval algorithms, including that of Mashburn et al., 2018 to obtain SSH, to understand the error sources and possible corrections when available. Because of the limited antenna gain of the CYGNSS instrument, the power SNR for general wind conditions is expected to be too low to accurately track the reflection point contribution, resulting in altimetry errors of the order of several meters. Other significant error sources are the CYGNSS satellites orbit knowledge, the ionosphere, the troposphere, the mean sea surface and the tides; some of these errors can be reduced by using models. The corrected SSH data will be assimilated into the ROMS high-resolution ocean model to investigate their ability to reproduce mesoscale eddies in the tropical oceans.

Voronovich, Alexander↗

Effectiveness of Interaural Delays Alone as Cues During Dynamic Sound Localization

The contribution of interaural time differences (ITDs) to the localization of virtual sound sources with and without head motion was examined. Listeners estimated the apparent azimuth, elevation and distance of virtual sources presented over headphones. Stimuli (3 sec., white noise) were synthesized from minimum-phase representations of nonindividualized head-related transfer functions (HRTFs); binaural magnitude spectra were derived from the minimum phase estimates and ITDs were represented as a pure delay. During dynamic conditions, listeners were encouraged to move their heads; head position was tracked and stimuli were synthesized in real time using a Convolvotron to simulate a stationary external sound source. Two synthesis conditions were tested: (1) both interaural level differences (ILDs) and ITDs correctly correlated with source location and head motion, (2) ITDs correct, no ILDs (flat magnitude spectrum). Head movements reduced azimuth confusions primarily when interaural cues were correctly correlated, although a smaller effect was also seen for ITDs alone. Externalization was generally poor for ITD-only conditions and was enhanced by head motion only for normal HRTFs. Overall the data suggest that, while ITDs alone can provide a significant cue for azimuth, the errors most commonly associated with virtual sources are reduced by location-dependent magnitude cues.

Wenzel, Elizabeth M.↗

Sub-luminal pulses from cosmic ray air showers

Some of the signals produced by air showers in scintillators possess a distinctive feature, a sub-luminal pulse (SLP) following the normal one with a time delay of approximately 1.5 r/c. The average amplitude of the SLP corresponds to an energy deposit of about 50 MeV, three times as much as is deposited in a typical scintillator by vertical minimum ionizing muons. The SLP account for approximately 5% of the energy deposited in the atmosphere by IR showers with energy 10 to the 10th power GeV at impact parameters 1 km. Assuming that these pulses are due to neutrons travelling with a speed slightly less than c, they provide a unique means of estimating E sub h, the energy deposited by slow hadrons, in showers of this very high energy. On the other hand, if not allowed for properly, these pulses are liable to cause errors in estimating the impact parameters of large showers from pulse width observations.

Linsley, J.↗

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↗

Airborne Radar for sUAS Sense and Avoid

A primary challenge for the safe integration of small UAS operations into the National Airspace System (NAS) is traffic deconfliction, both from manned and unmanned aircraft. The UAS Traffic Management (UTM) project being conducted at the National Aeronautics and Space Administration (NASA) considers a layered approach to separation provision, ranging from segregation of operations through airspace volumes (geofences) to autonomous sense and avoid (SAA) technologies for higher risk, densely occupied airspace. Cooperative SAA systems, such as Automatic Dependent Surveillance-Broadcast (ADS-B) and/or vehicle-to-vehicle communication systems provide significant additional risk mitigation but they fail to adequately mitigate collision risks for non-cooperative (non-transponder equipped) airborne aircraft. The RAAVIN (Radar on Autonomous Aircraft to Verify ICAROUS Navigation) flight test being conducted by NASA and the Mid-Atlantic Aviation Partnership (MAAP) was designed to investigate the applicability and performance of a prototype, commercially available sUAS radar to detect and track non-cooperative airborne traffic, both manned and unmanned. The radar selected for this research was a Frequency Modulated Continuous Wave (FMCW) radar with 120 degree azimuth and 80 degree elevation field of view operating at 24.55GHz center frequency with a 200 MHz bandwidth. The radar transmits 2 watts of power thru a Metamaterial Electronically Scanning Array antenna in horizontal polarization. When the radar is transmitting, personnel must be at least 1 meter away from the active array to limit nonionizing radiation exposure. The radar physical dimensions are 18.7cm by 12.1cm by 4.1cm and it weighs less than 820 grams making it well suited for installation on small UASs. The onboard, SAA capability, known as ICAROUS, (Independent Configurable Architecture for Reliable Operations of Unmanned Systems), developed by NASA to support sUAS operations, will provide autonomous guidance using the traffic radar tracks from the onboard radar. The RAAVIN set of studies will be conducted in three phases. The first phase included outdoor, ground-based radar evaluations performed at the Virginia Tech’s Kentland Farm testing range in Blacksburg, VA. The test was designed to measure how well the radar could detect and track a small UAS flying in the radar’s field of view. The radar was used to monitor 5 test flights consisting of outbound, inbound and crossing routes at different ranges and altitudes. The UAS flown during the ground test was the Inspire 2, a quad copter weighing less than 4250 grams (10 pounds) at maximum payload. The radar was set up to scan and track targets over its full azimuthal field of view from 0 to 40 degrees in elevation. The radar was configured to eliminate tracks generated from any targets located beyond 2000 meters from the radar and moving at velocities under 1.45 meters per second. For subsequent phases of the study the radar will be integrated with a sUAS platform to evaluate its performance in flight for SAA applications ranging from sUAS to manned GA aircraft detections and tracking. Preliminary data analysis from the first outdoor ground tests showed the radar performed well at tracking the vehicle as it flew outbound and repeatedly maintained a track out to 1000 meters (maximum 1387 meters) until the vehicle slowed to a stop to reverse direction to fly inbound. As the Inspire flew inbound tracks from beyond 800 meters, a reacquisition time delay was consistently observed between when the Inspire exceeds a speed of 1.45 meters per second and when the radar indicated an inbound target was present and maintained its track. The time delay varied between 6 seconds to over 37 seconds for the inbound flights examined, and typically resulted in about a 200 meter closure distance before the Inspire track was maintained. The radar performed well at both acquiring and tracking the vehicle as it flew crossing routes out past 400 meters across the azimuthal field of view. The radar and ICAROUS software will be integrated and flown on a BFD-1400-SE8-E UAS during the next phase of the RAAVIN project. The main goal at the conclusion of this effort is to determine if this radar technology can reliably support minimum requirements for SAA applications of sUAS. In particular, the study will measure the range of vehicle detections, lateral and vertical angular errors, false and missed/late detections, and estimated distance at closest point of approach after an avoidance maneuver is executed. This last metric is directly impacted by sensor performance and indicates its suitability for the task.

Szatkowski, George N.↗

Establishing Trust in NASA’s Artemis Program Computer-Human Interface (CHI) Implementation

The NASA Artemis program will return humans to the moon. This time, with the help of commercial and international partners, the program’s objective is a permanent moon base. The moon base infrastructure, including an orbiting moon station and moon surface assets, will be developed for astronauts to stay for the long haul to learn to live and work on another planet in preparation for an eventual Humans-to-Mars mission. As the roundtrip communication delays increase in deep space exploration, more onboard systems autonomy and functionality will be needed to maintain and control the vehicle or habitat. These mission constraints will change the current Earth-based spacecraft ground control support approach that will demand more safe, efficient, and effective Computer-Human Interface (CHI) control. For Artemis, CHI is defined as the elements that the crew interfaces with-audio, video, lighting, and crew controls. Understanding how CHI will need to evolve to support deep space missions will be critical for the Artemis program-especially crew controls which is the focus of this paper. How does NASA ensure crew controls are reliable to control complex systems and prevent a catastrophic event due to human error-especially when the astronauts could be physiologically and/or psychologically impaired? NASA’s approach to mitigating catastrophic hazards in human spaceflight system development such as crew controls is through a holistic system engineering and Human System Integration methodology that embraces NASA’s Human-Rating Requirements-ensuring human performance characteristics to control/safely recover the crew from hazardous situations within the human interface design are considered. This paper discusses, at a high level, CHI for the Artemis program. Next, a discussion of what it means to human-rate a space system crew controls and how trust in the human-computer interface begins with the NASA human rating requirements. Finally, a discussion on how systems engineering, and the human system integration process ensures that crew control implementation incorporates the NASA human-rating requirements.

Human-Rating↗