10.1007/s11227-022-04737-4">
 

Document Type

Article

Publication Date

2-2023

Abstract

Researchers typically increase training data to improve neural net predictive capabilities, but this method is infeasible when data or compute resources are limited. This paper 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. This research designs two experiments that vary the amount of training data samples and input features to determine the impact on the predictive power of the ADS-B classification model. The first experiment varies the number of training data observations from a limited feature set and results in 83.9% accuracy (within 10% of previous efforts with only 25% of the data). The findings show that feature selection and data quality lead to higher classification accuracy than data quantity. The second experiment accepted all ADS-B feature combinations and determined that airspeed, barometric pressure, and vertical speed had the most impact on aircraft engine type prediction.

Comments

This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Please fully attribute the citation below, including DOI in any re-use.

Funding Note: This study was funded by the Air Force Research Laboratory.

This article was first posted on AFIT Scholar in August 2022 as an in-press (accepted) version of record. The citation and file attachment now reflect the article as it appears in the journal issue.

[*] Author S. Bolton was an AFIT PhD candidate at the time of publication.

Source Publication

The Journal of Supercomputing

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