• Journal of Advanced Navigation Technology
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• v.24 no.5
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• pp.393-407
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• 2020

In order to reduce the threat by illegal unmanned aerial vehicles, a tracking system based on sound was implemented. There are three main points to the drone acoustic tracking method. First, it scans the space through variable beam formation to find a sound source and records the sound using a microphone array. Second, it classifies it into a hidden Markov model (HMM) to find out whether the sound source exists or not, and finally, the sound source is In the case of a drone, a sound source recorded and stored as a tracking reference signal based on an adaptive beam pattern is used. The simulation was performed in both the ideal condition without background noise and interference sound and the non-ideal condition with background noise and interference sound, and evaluated the tracking performance of illegal drones. The drone tracking system designed the criteria for determining the presence or absence of a drone according to the improvement of the search distance performance according to the microphone array performance and the degree of sound pattern matching, and reflected in the design of the speech reading circuit.

• The Journal of the Acoustical Society of Korea
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• v.43 no.3
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• pp.351-360
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• 2024

With the increasing use of drone technology, the Unmanned Aerial Vehicle (UAV) is now being utilized in various fields. However, this increased use of drones has resulted in various issues. Due to its small size, the drone is difficult to detect with radar or optical equipment, so acoustical tracking methods have been recently applied. In this paper, a method of localization of multiple drones using the acoustic characteristics of the quadcopter drone is suggested. Because the acoustic characteristics induced by each rotor are differentiated depending on the type of drone and its movement state, the sound source of the drone can be reconstructed by spatially clustering the results of the estimated positions of the blade passing frequency and its harmonic sound source. The reconstructed sound sources are utilized to finally determine the location of multiple-drone sound sources by applying the source localization algorithm. An experiment is conducted to analyze the acoustic characteristics of the test quadcopter drones, and the simulations for three different types of drones are conducted to localize the multiple drones based on the measured acoustic signals. The test result shows that the location of multiple drones can be estimated by utilizing the acoustic characteristics of the drone. Also, one can see that the clarity of the separated drone sound source and the source localization algorithm affect the accuracy of the localization for multiple-drone sound sources.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.881-890
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• 2022

As individual and group users actively use drones, the risks (Intrusion, Information leakage, and Sircraft crashes and so on) in no-fly zones are also increasing. Therefore, it is necessary to build a system that can detect drones intruding into the no-fly zone. General acoustic drone detection researches do not derive location-independent performance by directly learning drone sound including environmental noise in a deep learning model to overcome environmental noise. In this paper, we propose a drone detection system that collects sounds including environmental noise, and detects drones by removing noise from target sound. After removing environmental noise from the collected sound, the proposed system predicts the drone sound using Mel spectrogram and CNN deep learning. As a result, It is confirmed that the drone detection performance, which was weak due to unstudied environmental noises, can be improved by more than 7%.

• Journal of Advanced Navigation Technology
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• v.20 no.6
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• pp.604-611
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• 2016

Drone, which refers to unmanned aerial vehicles (UAV), industries are improving rapidly and exceeding existing level of remote controlled aircraft models. Also, they are applying automation and cloud network technology. Recently, the ability of drones can bring serious threats to public safety such as explosives and unmanned aircraft carrying hazardous materials. On the purpose of reducing these kinds of threats, it is necessary to detect these illegal drones, using acoustic feature extraction and classifying technology. In this paper, we introduce sound feature vector extraction method by harmonic feature extraction method (HLA). Feature vector extraction method based on HLA make it possible to distinguish drone sound, extracting features of sound data. In order to assess the performance of distinguishing sounds which exists in outdoor environment, we analyzed various sounds of things and real drones, and classified sounds of drone and others as simulation of each sound source.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5078-5094
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• 2019

In recent years, the development of drone technologies has promoted the widespread commercial application of drones. However, the ability of drone to carry explosives and other destructive materials may bring serious threats to public safety. In order to reduce these threats from illegal drones, acoustic feature extraction and classification technologies are introduced for drone sound identification. In this paper, we introduce the acoustic feature vector extraction method of harmonic line association (HLA), and subband power feature extraction based on wavelet packet transform (WPT). We propose a feature vector extraction method based on combined HLA and WPT to extract more sophisticated characteristics of sound. Moreover, to identify drone sounds, support vector machine (SVM) classification with the optimized parameter by genetic algorithm (GA) is employed based on the extracted feature vector. Four drones' sounds and other kinds of sounds existing in outdoor environment are used to evaluate the performance of the proposed method. The experimental results show that with the proposed method, identification probability can achieve up to 100 % in trials, and robustness against noise is also significantly improved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.364-370
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• 2020

This paper is related to detection and classification for micro-sized aircraft that flies at low-altitude. The deep-learning based method using sounds coming from the micro-sized aircraft is proposed to detect and identify them efficiently. We use MFCC as sound features and CNN as a detector and classifier. We've proved that each micro-drones have their own distinguishable MFCC feature and confirmed that we can apply CNN as a detector and classifier even though drone sound has time-related sequence. Typically many papers deal with RNN for time-related features, but we prove that if the number of frame in the MFCC features are enough to contain the time-related information, we can classify those features with CNN. With this approach, we've achieved high detection and classification ratio with low-computation power at the same time using the data set which consists of four different drone sounds. So, this paper presents the simple and effecive method of detection and classification method for micro-sized aircraft.

• Journal of Convergence for Information Technology
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• v.9 no.10
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• pp.101-107
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• 2019

This paper proposes a method for detecting faults in conveyor systems used for transportation of raw materials needed in the thermal power plant and cement industries. A small drone was designed in consideration of the difficulty in accessing the industrial site and the need to use it in wide industrial site. In order to apply the system to the embedded microprocessor, hardware and algorithms considering limited memory and execution time have been proposed. At this time, the failure determination method measures the peak frequency through the measurement, detects the continuity of the high frequency, and performs the failure diagnosis with the high frequency components of noise. The proposed system consists of experimental environment based on the data obtained from the actual thermal power plant, and it is confirmed that the proposed system is useful by conducting virtual environment experiments with the drone designed system. In the future, further research is needed to improve the drone's flight stability and to improve discrimination performance by using more intelligent methods of fault frequency.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.2
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• pp.85-92
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• 2022

The amphibian breeding habitat is confirmed by mating call. In some cases, the researcher directly identifies the amphibian individual, but in order to designate the habitat, it is necessary to map the mating call region of the amphibian population. Until now, it has been a popular methodology for researchers to hear mating calls and outline their breeding habitats. To improve this subjective methodology, we developed a technique for mapping mating call regions using Unmanned Aerial Vehicle (UAV). The technology uses a UAV, fitted with a sound recorder to record ground mating calls as it flies over an amphibian habitat. The core technology is to synchronize the recorded sound pressure with the flight log of the UAV and predict the sound pressure in a two-dimensional plane with probability density. For a demonstration study of this technology, artificial mating call was generated by a potable speaker on the ground and recorded by a UAV. Then, the recorded sound data was processed with an algorithm developed by us to map mating calls. As a result of the study, the correlation coefficient between the artificial mating call on the ground and the mating call map measured by the UAV was R=0.77. This correlation coefficient proves that our UAV recording system is sufficiently capable of detecting amphibian mating call regions.

• Speech Sciences
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• v.15 no.3
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• pp.145-156
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• 2008

The goal of this study was to investigate acoustic and physiologic characteristics of two phonation types of 'Khoomei' which is a traditional singing style of people who live around the Altai mountains or Mongolia region. It can be produced two pitches simultaneously - high melody pitch can be perceived along with a low drone pitch. Sygyt and kargyraa styles are the most popular and identifiable styles and they can be recognized as the different sounds depending on the method of voice production. Two trained Mongolians participated and have used at least 5 - 6 years. The characteristics of this voice production were measured by using flexible fiberscope, Stroboscopy, Lx Speech studio, Spead, and Doctor Speech. In Sygyt style, very high vocal fold closure (71.50%) with both true and false vocal folds contact and strong breathing support was observed. They also showed that tongue height and harmonics were increased (around 10dB) with resonance cavity movement. In contrast, it was found that Kargyraa sound had very low pitch with relaxed stomach, less laryngeal tension and lower vocal fold contact (69.50%) than hard Sygyt style sound without raising the tongue during phonation. 'Khoomei' phonation can be made by strong contact of both true and false vocal folds and by increasing the harmonics as well.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.10
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• pp.857-864
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• 2016

Many plane, UAV and drone fly in the sky as development of aviation industry. Plane and UAV fly and drone's propellers rotate so fast. Impact between flying objects which have high velocity threats passengers. Also the impact damages people, building and various property. Plane's operating speed is near sound velocity(340m/s), and propeller's rotating speed is less than that. Until now, impact experiment uses gas gun to get speed and the gun needs large space to entirely air expansion. Electromagnetic launcher, especially railgun, needs smaller space than gas gun to get enough speed about 500m/s. This paper explains electromagnetic launcher's operating principle, shows making electromagnetic launcher design guide line and suggests that it is a better apparatus to get low-high velocity.