Identification of jet aircraft model based on frequency characteristics of noise by convolutional neural network

Morinaga, Makoto; Mori, Junichi; Matsui, Takanori; Kawase, Yasuaki; Hanaka, Kazuyuki

doi:10.1250/ast.40.391

Cited by 7 publications

(6 citation statements)

References 7 publications

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“…We have been developing an aircraft model identification system that uses field measurements and machine learning, the aim being to use the frequency characteristics of aircraft noise to automatically classify the aircraft model associated with aircraft noise events. In Morinaga et al (2019) [2], we summarized the results of our previous studies [3][4][5] and those of studies conducted in other countries [6][7][8][9][10][11][12][13]. We also reported the results of two case studies that were conducted in the vicinity of airports, each of which identified a different model of jet aircraft.…”

Section: Previous Studiesmentioning

confidence: 99%

Aircraft model identification using convolutional neural network trained by those noises in a wide area around an airfield

Morinaga

Mori

Yamamoto

2023

Acoust. Sci. & Tech.

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We have developed an aircraft model identification system that uses a convolutional neural network (CNN). Our previous study used the CNN model to classify seven and eighteen models of jet aircraft by using training based on measured data taken directly under their flight paths near airports, reaching an accuracy of 98%. However, there were some limitations, such as the study results being obtained at only three sites, the measurements being taken only directly under the flight path, and the season being limited to winter. In this study, we examine if this method is also effective for identifying sound sources over a wider area not directly under the flight path. We conducted aircraft type identification by using the frequency characteristics of aircraft noise obtained at 90 measurement sites located within an area of approximately 30 km north-south and 6 km east-west, in summer and winter. We were able to achieve an identification rate of 98% for four types of sound sources. The results suggest that this system can identify measurement data over a wider area around airfields and can be applied to more practical issues, such as generating noise maps and validating predictions in the noise maps.

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Section: Previous Studiesmentioning

confidence: 99%

Aircraft model identification using convolutional neural network trained by those noises in a wide area around an airfield

Morinaga

Mori

Yamamoto

2023

Acoust. Sci. & Tech.

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“…The characteristic frequency distribution of these noise sources, especially the acoustically dominant sources, is used to develop acoustic noise [9] and noise immission models [10] that help predict the acoustic footprint of an aircraft. These unique acoustic signatures can also be used to identify specific aircraft [11,12]. The characteristic frequency distribution represents the superposition and linear and nonlinear interaction of all the acoustic modes.…”

Section: Contextmentioning

confidence: 99%

Recent Advances in Passive Acoustic Localization Methods via Aircraft and Wake Vortex Aeroacoustics

Joshi

Rahman

Hickey

2022

Fluids

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Passive acoustic aircraft and wake localization methods rely on the noise emission from aircraft and their wakes for detection, tracking, and characterization. This paper takes a holistic approach to passive acoustic methods and first presents a systematic bibliographic review of aeroacoustic noise of aircraft and drones, followed by a summary of sound generation of wing tip vortices. The propagation of the sound through the atmosphere is then summarized. Passive acoustic localization techniques utilize an array of microphones along with the known character of the aeroacoustic noise source to determine the characteristics of the aircraft or its wake. This paper summarizes the current state of knowledge of acoustic localization with an emphasis on beamforming and machine learning techniques. This review brings together the fields of aeroacoustics and acoustic-based detection the advance the passive acoustic localization techniques in aerospace.

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“…noise levels. However, when more complicated classification is required, such as distinguishing military aircraft other than fighter aircraft, it is necessary to deal with the aircraft model identification technique based on a continuous recording of frequency characteristics [8].…”

Section: Agenda Toward Aircraft Noise Mapping In Viet-mentioning

confidence: 99%

Effect of measurement-based noise source model of military airplanes on the validity of aircraft noise estimation in Vietnam

Bui

Nguyen

Morinaga

et al. 2021

Acoust. Sci. & Tech.

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Identification of jet aircraft model based on frequency characteristics of noise by convolutional neural network

Cited by 7 publications

References 7 publications

Aircraft model identification using convolutional neural network trained by those noises in a wide area around an airfield

Aircraft model identification using convolutional neural network trained by those noises in a wide area around an airfield

Recent Advances in Passive Acoustic Localization Methods via Aircraft and Wake Vortex Aeroacoustics

Effect of measurement-based noise source model of military airplanes on the validity of aircraft noise estimation in Vietnam

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