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ADS-B Classification Using Multivariate Long Short-Term Memory

This analysis extends previous research that used long short-term memory–fully convolutional networks to identify aircraft engine types from publicly available automatic dependent surveillance-broadcast (ADS-B) data.

A high-level overview of how ADS-B works. (Image: Air Force Institute of Technology)

Over the last three decades, storage on the internet increased by over 40,000 percent from 15.8 exabytes in 1993 to 6.8 zettabytes in 2020. While it is difficult to determine the exact number, as of February 2022, the size of the internet is estimated to be about 21 zettabytes and is doubling every two years. If we assume the average personal computer (PC) has a hard drive of one terabyte, 21 zettabytes is equivalent to 21 billion PCs, essentially three PCs for every person in the world. While a lot of this data is personal data, a large portion of it is considered publicly available information (PAI) and can be utilized by any internet user or organization.

This increase in available data has resulted in the study of identifying trends (i.e., data analytics) becoming more and more prevalent in multiple facets of society to include commerce and government. Researchers and major corporations have considered multiple ways to best utilize this massive resource, aptly referred to as ‘Big Data.’ Some areas that have shown promise include Internet of Things (IoT) analysis, traffic modeling, flight and maritime movement, image recognition, search engines and natural language processing.

The increased focus on PAI and data analytics is recognized by military defense strategists who are responsible for making sound defense decisions. By incorporating PAI with the plethora of sensor data at their disposal, such as data from intelligence, surveillance, and reconnaissance platforms, it is possible to improve the predictive power of those resources. The need for data analytics is apparent in the United States Air Force and Space Force where multi-domain operations are integral to their defense strategies. In fact, the FY22 Posture Statement calls out Command and Control’s need for the translation and sharing of data to provide ‘real-time dissemination of actionable information’ to provide ‘joint warfighting across all domains at a pace faster than our competitors’. Without recent advances in technology, artificial intelligence, and machine learning, this goal would be virtually impossible. Fortunately, new techniques can be used to filter the noise in big data much faster than human speed to quickly make inferences that are important to military decision makers.

To aid military leaders with analyzing the immense data at their disposal, we seek to improve military operations by providing enhanced capabilities for a major user of big data: intelligence analysts. One focus area important to intelligence analysts is pattern-of-life (POL) modeling. Some researchers seek to improve POL modeling via machine learning. Recent research interests suggest analyzing ground-based and onboard aircraft sensors with deep learning to predict aircraft characteristics.

One stream of research for POL modeling is focused on exploiting automatic dependent surveillance-broadcast (ADS-B) data to make predictions about aircraft. Aircraft within certain airspace are required to broadcast ADS-B Out via an onboard transponder. The benefit of using ADS-B data for classification problems is that it is publicly available and aircraft flying in the USA and Europe are required to broadcast it in most classes of airspace. ADS-B data is collected from various sites worldwide where hobbyists and researchers maintain a receiver to collect it. ADS-B collectors submit their data to centralized repositories, such as the ADS-B Exchange, that aggregate the data for public use. In these repositories, both statistical and kinematic information about the broadcasting aircraft is available.

This work was performed by Richard Dill, Michael R. Grimaila and Douglas Hodson for the Air Force Institute of Technology. For more information, download the Technical Support Package (free white paper) under the Communications category. ARL-96554

Topics:
Aerospace Artificial Intelligence Communications Computer software and hardware Computers Data Acquisition Defense Electronics & Computers Hardware Internet Internet of Things Materials handling PCs/Portable Computers Storage Telecommunications
This Brief includes a Technical Support Package (TSP).
Document cover
ADS-B Classification Using Multivariate Long Short-Term Memory

(reference ARL-96554) is currently available for download from the TSP library.

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    Aerospace & Defense Technology Magazine

    This article first appeared in the June, 2023 issue of Aerospace & Defense Technology Magazine (Vol. 8 No. 4).

    Read more articles from this issue here.

    Read more articles from the archives here.

    Overview

    The document presents a research study focused on improving the classification of aircraft engine types using Automatic Dependent Surveillance–Broadcast (ADS-B) data through advanced machine learning techniques. The authors, Sarah Bolton and colleagues, explore the application of multivariate long short-term memory (LSTM) networks combined with fully convolutional networks to enhance the accuracy of engine type identification.

    The study begins with a comprehensive literature review on ADS-B technology, which is crucial for tracking aircraft movements and gathering data on various flight parameters. The authors emphasize the growing importance of data analytics in aviation, particularly with the increasing volume of available data, often referred to as "Big Data." This data can be leveraged to identify trends and improve decision-making processes in various sectors, including aviation.

    A key aspect of the research is the methodology employed to develop and evaluate the classification models. The authors highlight the significance of feature selection, noting that irrelevant features can introduce noise and degrade model performance. Through their experiments, they identify critical features such as speed, pressure, and vertical speed, which are essential for accurately classifying aircraft engine types.

    The results section of the document presents the findings from the experiments conducted using the proposed models. The authors demonstrate that their approach can effectively classify engine types, even with limited training data, underscoring the importance of using high-quality features over merely increasing the volume of data.

    In conclusion, the study contributes to the field of aviation data analytics by providing insights into the effective use of machine learning for aircraft engine classification. The findings suggest that optimizing feature selection can lead to significant improvements in predictive accuracy, which is vital for enhancing safety and operational efficiency in aviation. The research not only advances the understanding of ADS-B data utilization but also sets the stage for future studies aimed at refining machine learning applications in the aviation industry.

    Overall, this document serves as a valuable resource for researchers and practitioners interested in the intersection of aviation, data analytics, and machine learning, highlighting the potential for innovative solutions in aircraft monitoring and classification.

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