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Multimodal physiological monitoring during virtual reality piloting tasks

August 25, 2022
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Journal Article
Author:
Hrishikesh Mohan Rao
…
Published in:
Multimodal Physiological Monitoring During Virtual Reality Piloting Tasks (version 1.0.0). PhysioNet. https://doi.org/10.13026/azwa-ge48.
Topic:
health
R&D area:
R&D group:

Summary

This dataset includes multimodal physiologic, flight performance, and user interaction data streams, collected as participants performed virtual flight tasks of varying difficulty. In virtual reality, individuals flew an "Instrument Landing System" (ILS) protocol, in which they had to land an aircraft mostly relying on the cockpit instrument readings. Participants were presented with four levels of difficulty, which were generated by varying wind speed, turbulence, and visibility. Each of the participants performed 12 runs, split into 3 blocks of four consecutive runs, one run at each difficulty, in a single experimental session. The sequence of difficulty levels was presented in a counterbalanced manner across blocks. Flight performance was quantified as a function of horizontal and vertical deviation from an ideal path towards the runway as well as deviation from the prescribed ideal speed of 115 knots. Multimodal physiological signals were aggregated and synchronized using Lab Streaming Layer. Descriptions of data quality are provided to assess each data stream. The starter code provides examples of loading and plotting the time synchronized data streams, extracting sample features from the eye tracking data, and building models to predict pilot performance from the physiology data streams.
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Summary

This dataset includes multimodal physiologic, flight performance, and user interaction data streams, collected as participants performed virtual flight tasks of varying difficulty. In virtual reality, individuals flew an "Instrument Landing System" (ILS) protocol, in which they had to land an aircraft mostly relying on the cockpit instrument readings. Participants were...

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Multimodal physiological monitoring during virtual reality piloting tasks

Fun as a strategic advantage: applying lessons in engagement from commercial games to military logistics training

July 1, 2022
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Journal Article
Author:
Jesslyn D. Alekseyev
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Published in:
Train., Educ., Learn. Sci., Vol. 59, 2022, p. 180-7.
Topic:
serious games
R&D area:
R&D group:

Summary

Digital games offer many elements to augment traditional classroom lectures and reading assignments. They enable players to explore concepts through repeat play in a low-risk environment, and allow players to integrate feedback given during gameplay and evaluate their own performance. Commercial games leverage a number of features to engage players and hold their attention. But do those engagement-improving methods have a place in instructional environments with a captive and motivated audience? Our experience building a logistics supply chain training game for the Marine Corps University suggests that yes; applying lessons in engagement from commercial games can both help improve player experience with the learning environment, and potentially improve learning outcomes.
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Summary

Digital games offer many elements to augment traditional classroom lectures and reading assignments. They enable players to explore concepts through repeat play in a low-risk environment, and allow players to integrate feedback given during gameplay and evaluate their own performance. Commercial games leverage a number of features to engage players...

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Fun as a strategic advantage: applying lessons in engagement from commercial games to military logistics training

AAM-Gym: Artificial intelligence testbed for advanced air mobility

June 9, 2022
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Journal Article
Author:
Marc W. Brittain
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Published in:
arXiv, 9 June 2022.
Topic:
artificial intelligence
R&D area:
R&D group:

Summary

We introduce AAM-Gym, a research and development testbed for Advanced Air Mobility (AAM). AAM has the potential to revolutionize travel by reducing ground traffic and emissions by leveraging new types of aircraft such as electric vertical take-off and landing (eVTOL) aircraft and new advanced artificial intelligence (AI) algorithms. Validation of AI algorithms require representative AAM scenarios, as well as a fast time simulation testbed to evaluate their performance. Until now, there has been no such testbed available for AAM to enable a common research platform for individuals in government, industry, or academia. MIT Lincoln Laboratory has developed AAM-Gym to address this gap by providing an ecosystem to develop, train, and validate new and established AI algorithms across a wide variety of AAM use-cases. In this paper, we use AAM-Gym to study the performance of two reinforcement learning algorithms on an AAM use-case, separation assurance in AAM corridors. The performance of the two algorithms is demonstrated based on a series of metrics provided by AAM-Gym, showing the testbed’s utility to AAM research.
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Summary

We introduce AAM-Gym, a research and development testbed for Advanced Air Mobility (AAM). AAM has the potential to revolutionize travel by reducing ground traffic and emissions by leveraging new types of aircraft such as electric vertical take-off and landing (eVTOL) aircraft and new advanced artificial intelligence (AI) algorithms. Validation of...

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AAM-Gym: Artificial intelligence testbed for advanced air mobility

Nearfield anechoic chamber and farfield on-site antenna calibration pattern comparison of an S-band planar phased array radar

April 27, 2022
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Conference Paper
Author:
Christine F. Parry
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Published in:
IEEE Annual Conf. on Wireless and Microwave Technology, WAMICON, 27-28 April 2022.
Topic:
antennas
R&D area:
R&D group:

Summary

The Advanced Technology Demonstrator (ATD) is an active, S-band, dual-polarization phased array radar developed for weather sensing. The ATD is an active electronically scanned array (AESA) with a 4-m aperture comprised of 4,864 individual transmit/receive (T/R) modules. The antenna was calibrated at the element, subarray, and array levels. Calibration, validation, and verification testing was completed in two main stages, first in an anechoic chamber and second after it was installed on site in its permanent location. This paper describes the procedure used to collect antenna patterns at each stage and compares three key performance metrics: beamwidth, mean-squared sidelobe level (MSSL), and integrated sidelobe level (ISL).
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Summary

The Advanced Technology Demonstrator (ATD) is an active, S-band, dual-polarization phased array radar developed for weather sensing. The ATD is an active electronically scanned array (AESA) with a 4-m aperture comprised of 4,864 individual transmit/receive (T/R) modules. The antenna was calibrated at the element, subarray, and array levels. Calibration, validation...

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Nearfield anechoic chamber and farfield on-site antenna calibration pattern comparison of an S-band planar phased array radar

Correlated Bayesian model of aircraft encounters in the terminal area given a straight takeoff or landing

March 12, 2022
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Journal Article
Author:
Andrew J. Weinert
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Published in:
Aerospace, Vol. 9, No.2, 12 March 2022.
Topic:
collision avoidance
R&D area:
R&D group:

Summary

The integration of new airspace entrants into terminal operations requires design and evaluation of Detect and Avoid systems that prevent loss of well clear from and collision with other aircraft. Prior to standardization or deployment, an analysis of the safety performance of those systems is required. This type of analysis has typically been conducted by Monte Carlo simulation with synthetic, statistically representative encounters between aircraft drawn from an appropriate encounter model. While existing encounter models include terminal airspace classes, none explicitly represents the structure expected while engaged in terminal operations, e.g., aircraft in a traffic pattern. The work described herein is an initial model of such operations where an aircraft landing or taking off via a straight trajectory encounters another aircraft landing or taking off, or transiting by any means. The model shares the Bayesian network foundation of other Massachusetts Institute of Technology Lincoln Laboratory encounter models but tailors those networks to address structured terminal operations, i.e., correlations between trajectories and the airfield and each other. This initial model release is intended to elicit feedback from the standards-writing community.
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Summary

The integration of new airspace entrants into terminal operations requires design and evaluation of Detect and Avoid systems that prevent loss of well clear from and collision with other aircraft. Prior to standardization or deployment, an analysis of the safety performance of those systems is required. This type of analysis...

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Correlated Bayesian model of aircraft encounters in the terminal area given a straight takeoff or landing

Radar coverage analysis for the Terminal Precipitation on the Glass Program

March 4, 2022
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Project Report
Author:
John Y. Cho
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Published in:
MIT Lincoln Laboratory Report ATC-450
Topic:
radar
R&D area:
R&D group:

Summary

The Terminal Precipitation on the Glass (TPoG) program proposes to improve the STARS precipitation depiction by adding an alternative precipitation product based on a national weather-radar-based mosaic, i.e., the NextGen Weather System (aka NextGen Weather Processor [NWP] and Common Support Services Weather [CSS-Wx]). This report describes spatial and temporal domain analyses conducted over the 146 terminal radar approach control (TRACON) airspaces that are within scope of TPoG to identify and quantify future TPoG benefits, as well as potential operational issues.
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Summary

The Terminal Precipitation on the Glass (TPoG) program proposes to improve the STARS precipitation depiction by adding an alternative precipitation product based on a national weather-radar-based mosaic, i.e., the NextGen Weather System (aka NextGen Weather Processor [NWP] and Common Support Services Weather [CSS-Wx]). This report describes spatial and temporal domain...

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Radar coverage analysis for the Terminal Precipitation on the Glass Program

Adapting deep learning models to new meteorological contexts using transfer learning

December 15, 2021
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Conference Paper
Author:
Pooya R. Khorrami
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Published in:
2021 IEEE International Conference on Big Data (Big Data), 2021, pp. 4169-4177, doi: 10.1109/BigData52589.2021.9671451.
Topic:
machine learning
R&D area:
R&D group:

Summary

Meteorological applications such as precipitation nowcasting, synthetic radar generation, statistical downscaling and others have benefited from deep learning (DL) approaches, however several challenges remain for widespread adaptation of these complex models in operational systems. One of these challenges is adequate generalizability; deep learning models trained from datasets collected in specific contexts should not be expected to perform as well when applied to different contexts required by large operational systems. One obvious mitigation for this is to collect massive amounts of training data that cover all expected meteorological contexts, however this is not only costly and difficult to manage, but is also not possible in many parts of the globe where certain sensing platforms are sparse. In this paper, we describe an application of transfer learning to perform domain transfer for deep learning models. We demonstrate a transfer learning algorithm called weight superposition to adapt a Convolutional Neural Network trained in a source context to a new target context. Weight superposition is a method for storing multiple models within a single set of parameters thus greatly simplifying model maintenance and training. This approach also addresses the issue of catastrophic forgetting where a model, once adapted to a new context, performs poorly in the original context. We apply weight superposition to the problem of synthetic weather radar generation and show that in scenarios where the target context has less data, a model adapted with weight superposition is better at maintaining performance when compared to simpler methods. Conversely, the simple adapted model performs better on the source context when the source and target contexts have comparable amounts of data.
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Summary

Meteorological applications such as precipitation nowcasting, synthetic radar generation, statistical downscaling and others have benefited from deep learning (DL) approaches, however several challenges remain for widespread adaptation of these complex models in operational systems. One of these challenges is adequate generalizability; deep learning models trained from datasets collected in specific...

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Adapting deep learning models to new meteorological contexts using transfer learning

CoSPA data product description

November 1, 2021
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Project Report
Author:
Diana L. Klingle-Wilson
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Published in:
MIT Lincoln Laboratory Report ATC-449
Topic:
aviation weather
R&D area:
R&D group:

Summary

This document contains a description of Consolidated Storm Prediction for Aviation (CoSPA) data products that are packaged and distributed for external users. As described in Rappa and Troxel, 2013 [1] for Corridor Integrated Weather System (CIWS) data products, CoSPA products are categorized as gridded and non-gridded. Gridded products are typically expressed as rectangular arrays whose elements contain a data value coinciding with uniformly-spaced observations or computed results on a 2-D surface. Gridded data arrays map to the earth's surface through a map projection, for example, Lambert Conformal or Lambert Azimuthal Equal-Area. CoSPA generates only gridded products; there are no non-gridded data for CoSPA.
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Summary

This document contains a description of Consolidated Storm Prediction for Aviation (CoSPA) data products that are packaged and distributed for external users. As described in Rappa and Troxel, 2013 [1] for Corridor Integrated Weather System (CIWS) data products, CoSPA products are categorized as gridded and non-gridded. Gridded products are typically...

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CoSPA data product description

Effect of a wet spherical radome on the reflected power for an S-band planar phased array radar antenna

October 24, 2021
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Conference Paper
Author:
Christine F. Parry
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Published in:
2021 Antenna Measurement Techniques Association Symp., AMTA, 24-29 October 2021.
Topic:
antennas
R&D area:
R&D group:

Summary

An active S-band dual-polarized multifunction phased array radar (MPAR), the Advanced Technology Demonstrator (ATD), has recently been developed for weather sensing and aircraft surveillance. The ATD is an active electronically scanned array (AESA) with 4864 transmit/receive (T/R) modules and was installed in a spherical radome. Simulations and a novel phased array measurement technique have been explored to assess the impact of high reflectivity from a wet radome during rain that can potentially induce voltages exceeding the transmit amplifier breakdown voltage. The measurement technique uses array elements radiating one at a time to illuminate the radome, and uses superposition to quantify the received signal power in a reference antenna on the face of the array. It is shown that when the radome surface is wet and highly reflective, certain electronic steering angles sum to a large reflected signal focused on the array face. This measurement technique can be used prior to high-power phased array radar operation to monitor the magnitude of reflections and help avoid element transmit amplifier failures.
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Summary

An active S-band dual-polarized multifunction phased array radar (MPAR), the Advanced Technology Demonstrator (ATD), has recently been developed for weather sensing and aircraft surveillance. The ATD is an active electronically scanned array (AESA) with 4864 transmit/receive (T/R) modules and was installed in a spherical radome. Simulations and a novel phased...

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Effect of a wet spherical radome on the reflected power for an S-band planar phased array radar antenna

Benchmarking the processing of aircraft tracks with triples mode and self-scheduling

September 20, 2021
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Conference Paper
Author:
Andrew J. Weinert
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Published in:
2021 IEEE High Performance Extreme Computing Conf., HPEC, 20-24 September 2021.
Topic:
open systems
R&D area:
R&D group:

Summary

As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Developing and certifying collision avoidance systems often rely on the extensive use of Monte Carlo collision risk analysis simulations using probabilistic models of aircraft flight. To train these models, high performance computing resources are required. We've prototyped a high performance computing workflow designed and deployed on the Lincoln Laboratory Supercomputing Center to process billions of observations of aircraft. However, the prototype has various computational and storage bottlenecks that limited rapid or more comprehensive analyses and models. In response, we've developed a novel workflow to take advantage of various job launch and task distribution technologies to improve performance. The workflow was benchmarked using two datasets of observations of aircraft, including a new dataset focused on the environment around aerodromes. Optimizing how the workflow was parallelized drastically reduced the execution time from weeks to days.
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Summary

As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Developing and certifying collision avoidance systems often rely on the extensive use of...

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Benchmarking the processing of aircraft tracks with triples mode and self-scheduling
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