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Applicability and surrogacy of uncorrelated airspace encounter models at low altitudes
July 1, 2021
Journal Article
Published in:
J. Air Transport., Vol. 29, No. 3, July-September 2021, pp. 137-41.
Summary
National Airspace System (NAS) is a complex and evolving system that enables safe and efficient aviation. Advanced air mobility concepts and new airspace entrants, such as unmanned aircraft, must integrate into the NAS without degrading overall safety or efficiency. For instance, regulations, standards, and systems are required to mitigate the risk of a midair collision between aircraft. Monte Carlo simulations have been a foundational capability for decades to develop, assess, and certify aircraft conflict avoidance systems. These are often validated through human-in-the-loop experiments and flight testing. For example, an update to the Traffic Collision Avoidance System (TCAS) mandated for manned aircraft was validated in part using this approach [1]. For many aviation safety studies, manned aircraft behavior is represented using the MIT Lincoln Laboratory statistical encounter models [2–5]. The original models [2–4] were developed from 2008 to 2013 to support safety simulations for altitudes above 500 feet above ground level (AGL). However, these models were not sufficient to assess the safety of smaller unmanned aerial systems (UAS) operations below 500 feet AGL and fully support the ASTM F38 and RTCA SC-147 standards efforts. In response, newer models [5–7] with altitude floors below 500 feet AGL have been in development since 2018. Many of the models assume that aircraft behavior is uncorrelated and not dependent on air traffic services or nearby aircraft. The models were trained using observations of cooperative aircraft equipped with transponders, but data sources and assumptions vary. The newer models are organized by aircraft types of fixed-wing multi-engine, fixed-wing single engine, and rotorcraft, whereas the original models do not consider aircraft type. Our research objective was to compare the various uncorrelated models of conventional aircraft and identify how the models differ. Particularly if models of rotorcraft were sufficiently different from models of fixed-wing aircraft to require type-specific models. The scope of this work was limited to altitudes below 5000 feet AGL, the expected altitude ceiling for many new airspace entrants. The scope was also informed by the Federal Aviation Administration (FAA) UAS Integration Office and Alliance for System Safety of UAS through Research Excellence (ASSURE). The primary contribution is guidance on which uncorrelated models to leverage when evaluating the performance of a collision avoidance system designed for low-altitude operations, such as prescribed by the ASTM F3442 detect and avoid standard for smaller UAS [8]. We also address which models can be surrogates for non-cooperative aircraft without transponders. All models and software used are publicly available under open source licenses [9].
Summary
National Airspace System (NAS) is a complex and evolving system that enables safe and efficient aviation. Advanced air mobility concepts and new airspace entrants, such as unmanned aircraft, must integrate into the NAS without degrading overall safety or efficiency. For instance, regulations, standards, and systems are required to mitigate the...
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Seasonal Inhomogeneous Nonconsecutive Arrival Process Search and Evaluation
January 15, 2021
Conference Paper
Published in:
25th International Conference on Pattern Recognition [submitted]
Topic:
R&D area:
Summary
Time series often exhibit seasonal patterns, and identification of these patterns is essential to understanding thedata and predicting future behavior. Most methods train onlarge datasets and can fail to predict far past the training data. This limitation becomes more pronounced when data is sparse. This paper presents a method to fit a model to seasonal time series data that maintains predictive power when data is limited. This method, called SINAPSE, combines statistical model fitting with an information criteria to search for disjoint, andpossibly nonconsecutive, regimes underlying the data, allowing for a sparse representation resistant to overfitting.
Summary
Time series often exhibit seasonal patterns, and identification of these patterns is essential to understanding thedata and predicting future behavior. Most methods train onlarge datasets and can fail to predict far past the training data. This limitation becomes more pronounced when data is sparse. This paper presents a method to...
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Seasonal Inhomogeneous Nonconsecutive Arrival Process Search and Evaluation
August 26, 2020
Conference Paper
Published in:
International Conference on Artificial Intelligence and Statistics, 26-28 August 2020 [submitted]
Topic:
R&D area:
Summary
Seasonal data may display different distributions throughout the period of seasonality. We fit this type of model by determiningthe appropriate change points of the distribution and fitting parameters to each interval. This offers the added benefit of searching for disjoint regimes, which may denote the samedistribution occurring nonconsecutively. Our algorithm outperforms SARIMA for prediction.
Summary
Seasonal data may display different distributions throughout the period of seasonality. We fit this type of model by determiningthe appropriate change points of the distribution and fitting parameters to each interval. This offers the added benefit of searching for disjoint regimes, which may denote the samedistribution occurring nonconsecutively. Our algorithm...
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Monetized weather radar network benefits for tornado cost reduction
August 29, 2019
Project Report
Published in:
MIT Lincoln Laboratory Report NOAA-35
Summary
A monetized tornado benefit model is developed for arbitrary weather radar network configurations. Geospatial regression analyses indicate that improvement in two key radar coverage parameters--fraction of vertical space observed and cross-range horizontal resolution--lead to better tornado warning performance as characterized by tornado detection probability and false alarm ratio. Previous experimental results showing faster volume scan rates yielding greater warning performance, including increased lead times, are also incorporated into the model. Enhanced tornado warning performance, in turn, reduces casualty rates. In combination, then, it is clearly established that better and faster radar observations reduce tornado casualty rates. Furthermore, lower false alarm ratios save costs by cutting down on people's time lost when taking shelter.
Summary
A monetized tornado benefit model is developed for arbitrary weather radar network configurations. Geospatial regression analyses indicate that improvement in two key radar coverage parameters--fraction of vertical space observed and cross-range horizontal resolution--lead to better tornado warning performance as characterized by tornado detection probability and false alarm ratio. Previous experimental...
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Shining light on thermophysical Near-Earth Asteroid modeling efforts
January 22, 2019
Conference Paper
Published in:
1st NEO and Debris Detection Conf., 22-24 January 2019.
Topic:
R&D area:
R&D group:
Summary
Comprehensive thermophysical analyses of Near-Earth Asteroids (NEAs) provide important information about their physical properties, including visible albedo, diameter, composition, and thermal inertia. These details are integral to defining asteroid taxonomy and understanding how these objects interact with the solar system. Since infrared (IR) asteroid observations are not widely available, thermophysical modeling techniques have become valuable in simulating properties of different asteroid types. Several basic models that assume a spherical asteroid shape have been used extensively within the research community. As part of a program focused on developing a simulation of space-based IR sensors for asteroid search, the Near-Earth Asteroid Model (NEATM) developed by Harris, A. in 1998, was selected. This review provides a full derivation of the formulae behind NEATM, including the spectral flux density equation, consideration of the solar phase angle, and the geometry of the asteroid, Earth, and Sun system. It describes how to implement the model in software and explores the use of an ellipsoidal asteroid shape. It also applies the model to several asteroids observed by NASA's Near-Earth Object Wide-field Survey Explorer (NEOWISE) and compares the performance of the model to the observations.
Summary
Comprehensive thermophysical analyses of Near-Earth Asteroids (NEAs) provide important information about their physical properties, including visible albedo, diameter, composition, and thermal inertia. These details are integral to defining asteroid taxonomy and understanding how these objects interact with the solar system. Since infrared (IR) asteroid observations are not widely available, thermophysical...
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Modeling and validation of a mm-wave shaped dielectric lens antenna
July 29, 2018
Conference Paper
Published in:
2018 Int. Applied Computational Electromagnetics Society Symp., ACES, 29 July - 1 August 2018.
Summary
The modeling and validation of a 33 GHz shaped dielectric antenna design is investigated. The electromagnetic modeling was performed in both WIPL-D and FEKO, and was used to validate the antenna design prior to fabrication of the lens. It is shown that both WIPL-D and FEKO yield similarly accurate results as compared to measured far-field gain radiation patterns.
Summary
The modeling and validation of a 33 GHz shaped dielectric antenna design is investigated. The electromagnetic modeling was performed in both WIPL-D and FEKO, and was used to validate the antenna design prior to fabrication of the lens. It is shown that both WIPL-D and FEKO yield similarly accurate results...
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Quantification of radar QPE performance based on SENSR network design possibilities
April 23, 2018
Conference Paper
Published in:
2018 IEEE Radar Conf., RadarConf, 23-27 April 2018.
Summary
In 2016, the FAA, NOAA, DoD, and DHS initiated a feasibility study for a Spectrum Efficient National Surveillance Radar (SENSR). The goal is to assess approaches for vacating the 1.3- to 1.35-GHz radio frequency band currently allocated to FAA/DoD long-range radars so that this band can be auctioned for commercial use. As part of this goal, the participating agencies have developed preliminary performance requirements that not only assume minimum capabilities based on legacy radars, but also recognize the need for enhancements in future radar networks. The relatively low density of the legacy radar networks, especially the WSR-88D network, had led to the goal of enhancing low-altitude weather coverage. With multiple design metrics and network possibilities still available to the SENSR agencies, the benefits of low-altitude coverage must be assessed quantitatively. This study lays the groundwork for estimating Quantitative Precipitation Estimation (QPE) differences based on network density, array size, and polarimetric bias. These factors create a pareto front of cost-benefit for QPE in a new radar network, and these results will eventually be used to determine appropriate tradeoffs for SENSR requirements. Results of this study are presented in the form of two case examples that quantify errors based on polarimetric bias and elevation, along with a description of eventual application to a national network in upcoming expansion of the work.
Summary
In 2016, the FAA, NOAA, DoD, and DHS initiated a feasibility study for a Spectrum Efficient National Surveillance Radar (SENSR). The goal is to assess approaches for vacating the 1.3- to 1.35-GHz radio frequency band currently allocated to FAA/DoD long-range radars so that this band can be auctioned for commercial...
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Hybrid mixed-membership blockmodel for inference on realistic network interactions
April 5, 2018
Journal Article
Published in:
IEEE Trans. Netw. Sci. Eng., Vol. 6, No. 3, July-Sept. 2019.
Topic:
R&D area:
Summary
This work proposes novel hybrid mixed-membership blockmodels (HMMB) that integrate three canonical network models to capture the characteristics of real-world interactions: community structure with mixed-membership, power-law-distributed node degrees, and sparsity. This hybrid model provides the capacity needed for realism, enabling control and inference on individual attributes of interest such as mixed-membership and popularity. A rigorous inference procedure is developed for estimating the parameters of this model through iterative Bayesian updates, with targeted initialization to improve identifiability. For the estimation of mixed-membership parameters, the Cramer-Rao bound is derived by quantifying the information content in terms of the Fisher information matrix. The effectiveness of the proposed inference is demonstrated in simulations where the estimates achieve covariances close to the Cramer-Rao bound while maintaining good truth coverage. We illustrate the utility of the proposed model and inference procedure in the application of detecting a community from a few cue nodes, where success depends on accurately estimating the mixed-memberships. Performance evaluations on both simulated and real-world data show that inference with HMMB is able to recover mixed-memberships in the presence of challenging community overlap, leading to significantly improved detection performance over algorithms based on network modularity and simpler models.
Summary
This work proposes novel hybrid mixed-membership blockmodels (HMMB) that integrate three canonical network models to capture the characteristics of real-world interactions: community structure with mixed-membership, power-law-distributed node degrees, and sparsity. This hybrid model provides the capacity needed for realism, enabling control and inference on individual attributes of interest such as...
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The Offshore Precipitation Capability
September 16, 2016
Project Report
Published in:
MIT Lincoln Laboratory Report ATC-430
Summary
In this work, machine learning and image processing methods are used to estimate radar-like precipitation intensity and echo top heights beyond the range of weather radar. The technology, called the Offshore Precipitation Capability (OPC), combines global lightning data with existing radar mosaics, five Geostationary Operational Environmental Satellite (GOES) channels, and several fields from the Rapid Refresh (RAP) 13 km numerical weather prediction model to create precipitation and echo top fields similar to those provided by existing Federal Aviation Administration (FAA) weather systems. Preprocessing and feature extraction methods are described to construct inputs for model training. A variety of machine learning algorithms are investigated to identify which provides the most accuracy. Output from the machine learning model is blended with existing radar mosaics to create weather radar-like analyses that extend into offshore regions. The resulting fields are validated using land radars and satellite precipitation measurements provided by the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement Mission (GPM) core observatory satellite. This capability is initially being developed for the Miami Oceanic airspace with the goal of providing improved situational awareness for offshore air traffic control.
Summary
In this work, machine learning and image processing methods are used to estimate radar-like precipitation intensity and echo top heights beyond the range of weather radar. The technology, called the Offshore Precipitation Capability (OPC), combines global lightning data with existing radar mosaics, five Geostationary Operational Environmental Satellite (GOES) channels, and...
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En route sector capacity model final report
January 29, 2016
Project Report
Published in:
MIT Lincoln Laboratory Report ATC-426
Summary
Accurate predictions of en route sector capacity are vital when analyzing the benefits of proposed new air traffic management decision-support tools or new airspace designs. Controller workload is the main determinant of sector capacity. This report describes a new workload-based capacity model that improves upon the Federal Aviation Administration's current Monitor Alert capacity model. Analysts often use Monitor Alert sector capacities in evaluating the benefits of decision-support aids or airspace designs. However, Monitor Alert, which was designed to warn controllers of possible sector overload, sets sector capacity limits based solely on handoff workload and fixed procedural constraints. It ignores the effects of conflict workload and recurring workload (from activities such as monitoring, vectoring, spacing, and metering). Each workload type varies differently as traffic counts and airspace designs are changed. When used for benefits analysis, Monitor Alert's concentration on a single workload type can lead to erroneous conclusions. The new model considers all three workload types. We determine the relative contribution of the three workload types by fitting the model to the upper frontiers that appear in peak daily sector traffic counts from today's system. When we fit the Monitor Alert model to these same peak traffic counts, it can only explain the observed frontiers by hypothesizing large handoff workload. Large handoff workload would imply that decision-support aids should focus on handoff tasks. The new model fits the traffic data with less error, and shows that recurring tasks create significantly more workload in all sectors than do handoff tasks. The new model also shows that conflict workload dominates in very small sectors. These findings suggest that it is more beneficial to develop decision-support aids for recurring tasks and conflict tasks than for handoff tasks.
Summary
Accurate predictions of en route sector capacity are vital when analyzing the benefits of proposed new air traffic management decision-support tools or new airspace designs. Controller workload is the main determinant of sector capacity. This report describes a new workload-based capacity model that improves upon the Federal Aviation Administration's current...
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