Processing and Recognition of Small Unmanned Vehicles Sound Signals

Карташов, В. М.; Oleynikov, Vladimir; Koryttsev, I. V.; Zubkov, Oleh; Бабкін, С. І.; Sheiko, Sergiy

doi:10.1109/infocommst.2018.8632162

Cited by 13 publications

(3 citation statements)

References 1 publication

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“…cos nω front t + cos (nω rear t) . 17This still includes a large number of harmonics, which could be reduced using a bandpass filter on a recorded signal, and equation (17) would be similar to equation (14). For example, taking into account that the rotation speed of small and medium quadcopters ranges from 4,000 to 12,000 RPM, only frequencies in the range from 100 Hz to 500 Hz can be included.…”

Section: Algorithmmentioning

confidence: 99%

Analysis of a Quadcopter’s Acoustic Signature in Different Flight Regimes

et al. 2020

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Section: Algorithmmentioning

confidence: 99%

Analysis of a Quadcopter’s Acoustic Signature in Different Flight Regimes

et al. 2020

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show abstract

“…Accuracies of 83% and 61% were obtained for PIL and KNN, respectively [18]. In 2018, it was processed and identified the sound signals of quadcopters and monocopters, providing closer real-time operation through fast algorithms [19]. The spectra of both aircraft include harmonic components with multiple propeller rotation frequencies and large areas of specific noise.…”

Section: Passive Sound Detectionmentioning

confidence: 99%

Intelligent Identification and Classification of Small UAV Remote Control Signals Based on Improved Yolov5-7.0

Li,

Hao,

Wang

et al. 2024

IEEE Access

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At present, an increasing number of small UAVs(Unmanned Aerial Vehicles) are commercialized and common, and the application of small UAVs has very good development prospects, such as UAV distribution services, UAV aerial photography services, and UAV formation performance. However, the misuse of small drones poses a significant threat. Lawbreakers use small drones equipped with various sensors to spy on personal privacy, steal corporate secrets, and threaten national conferences, which have had many adverse effects on society. In future wars, these small drones will perhaps be used on the battlefield along with high-end weapons. Therefore, it is necessary to find a solution for effectively identifying the basic information of UAV. For the existence of UAV and various small UAV types, this paper proposes a combination of RF sensing and target detection techniques with target detection algorithms to learn RF signal frequency frequency hopping features to detect UAV presence and identify the detected UAV. First, the RF signal of the UAV was obtained in real time by a software radio, and the time-frequency analysis of the short-time Fourier transform and wavelet transform is performed to generate frequency domain images with retained frequency hopping features. Then, the improved Yolov5-7.0 target detection model was employed for training, and finally, the trained model was used for identification and classification. The results showed that the method can effectively assist the detection and classification of UAVs that were obtained by identifying and classifying 5400 unlabeled images. The F1 score was 0.93, and the three assessment measures of P(Precision), R(Recall), and mAP(mean Average Precision) were 1.00, 1.00, and 0.967, respectively.INDEX TERMS Small UAV, RF sensing, object detection, frequency hopping feature, short-time Fourier transform, wavelet transform, software radio.

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“…На четвертом этапе вычисляется БПФ от АКФ каждого сегмента. Как показано в [2], вычисление БПФ от первой, второй и т.д. АКФ позволяет повысить отношение сигнал/шум при выделении гармонического сигнала на фоне шумов.…”

Section: алгоритм обнаружения аи бплаunclassified

Investigation of the efficiency of detection and recognition of small-sized unmanned aerial vehicles by their acoustic radiation

Олейников¹,

Zubkov²,

Карташов³

et al. 2018

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The features of acoustic spectrum of UAVs, spectrum of natural and industrial acoustic noise, noise spectrum of automobile and rail transport, and human speech spectrum were investigated. The method for recognition of UAV sound based on the Mel-frequency cepstral coefficients was proposed. The universal method for detecting UAV based on characteristic features of acoustic spectrum was proposed as well. Both methods were tested using experimental recordings of UAVs and noise sounds and got close well results. The universal recognition method has some worse recognition reliability and false alarm probability, but does not need creation of sound and noise images base.

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Processing and Recognition of Small Unmanned Vehicles Sound Signals

Cited by 13 publications

References 1 publication

Analysis of a Quadcopter’s Acoustic Signature in Different Flight Regimes

Analysis of a Quadcopter’s Acoustic Signature in Different Flight Regimes

Intelligent Identification and Classification of Small UAV Remote Control Signals Based on Improved Yolov5-7.0

Investigation of the efficiency of detection and recognition of small-sized unmanned aerial vehicles by their acoustic radiation

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