CNN-based UAV Detection with Short Time Fourier Transformed Acoustic Features

Kim, Bobae; Jang, Beomhee; Lee, Donggeon; Im, Sungbin

doi:10.1109/iceic49074.2020.9051099

Cited by 4 publications

(7 citation statements)

References 4 publications

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“…Three methods are also evaluated for reference, including PCA‐SVM classifier [28], CA‐CFAR [29], and STFT‐CNN [30]. The STFT‐CNN model employs a CNN network consisting of two convolutional layers, two pooling layers, two fully connected layers and a classification head, and STFT is performed on echo signals to facilitate semantic feature extraction by CNN.…”

Section: Performance Analysis Results and Discussionmentioning

confidence: 99%

“…We also illustrate the comparative results between contrast learning-based FEF method and the reference STFT + CNN method [30] in the case of severe ground clutter, where heat maps are employed. The visualization results of feature vectors are shown in Figure 12, where subfigure (a) and (b) show the 1D feature vectors by STFT + CNN and FEF, respectively.…”

Section: Experimental Results On Feature Extractionmentioning

confidence: 99%

“… Heat maps of feature vectors. (a) short‐time Fourier transform (STFT) + CNN method [30], (b) proposed feature extraction and fusion (FEF) method …”

Section: Performance Analysis Results and Discussionmentioning

confidence: 99%

“…Three methods are also evaluated for reference, including PCA-SVM classifier [28], CA-CFAR [29], and STFT-CNN [30]. The STFT-CNN model employs a CNN network…”

Section: Reference Methodsmentioning

confidence: 99%

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Deep learning‐based unmanned aerial vehicle detection in the low altitude clutter background

Peng

Wang

2022

IET Signal Processing

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Unmanned aerial vehicles (UAVs), widely used due to their low cost and versatility, pose security and privacy threats, which calls for their reliable recognition at low altitudes. However, strong ground clutter and multipath effects severely interfere with the weak radar echoes reflected off the micro-UAVs, resulting in severe degradation of recognition reliability. Based on channel modelling and UAV recognisability analysis, a time-frequency transform-aided contrastive learning model is proposed to suppress the severe ground clutter and reliably recognise low-altitude UAVs. In the proposed framework, a timefrequency transform unit is first applied to suppress the multipath-induced ambiguity effect and ease the semantic feature extraction via Zhao-Atlas-Marks transform and morphological operation. Thereafter, a contrastive-learning-based feature extraction and fusion unit is established to suppress non-target clutter interference and extract recognisable semantic UAV features. Finally, a gated recurrent unit-based classifier is designed for UAV recognition. Sufficient experiments are carried out on both real and simulated data sets, and the comparative results verify that the proposed model outperforms the mainstream algorithms and improves the detection accuracy by more than 5% under severe ground clutter interference.

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Section: Performance Analysis Results and Discussionmentioning

confidence: 99%

Section: Experimental Results On Feature Extractionmentioning

confidence: 99%

“… Heat maps of feature vectors. (a) short‐time Fourier transform (STFT) + CNN method [30], (b) proposed feature extraction and fusion (FEF) method …”

Section: Performance Analysis Results and Discussionmentioning

confidence: 99%

“…Three methods are also evaluated for reference, including PCA-SVM classifier [28], CA-CFAR [29], and STFT-CNN [30]. The STFT-CNN model employs a CNN network…”

Section: Reference Methodsmentioning

confidence: 99%

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Deep learning‐based unmanned aerial vehicle detection in the low altitude clutter background

Peng

Wang

2022

IET Signal Processing

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“…In recent years, many research works have been published to address UAV detection, tracking, and classification problems. The main drone detection technologies are: radar sensors [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], RF sensors [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ], audio sensors [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ], and camera sensors using visual UAV characteristics [ 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ]. Based on the above-mentioned sources, the advantages and disadvantages of each drone detection technology are compared in Table 2 .…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Seidaliyeva,

Ilipbayeva,

Taissariyeva

et al. 2023

Sensors

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The fast development of unmanned aerial vehicles (UAVs), commonly known as drones, has brought a unique set of opportunities and challenges to both the civilian and military sectors. While drones have proven useful in sectors such as delivery, agriculture, and surveillance, their potential for abuse in illegal airspace invasions, privacy breaches, and security risks has increased the demand for improved detection and classification systems. This state-of-the-art review presents a detailed overview of current improvements in drone detection and classification techniques: highlighting novel strategies used to address the rising concerns about UAV activities. We investigate the threats and challenges faced due to drones’ dynamic behavior, size and speed diversity, battery life, etc. Furthermore, we categorize the key detection modalities, including radar, radio frequency (RF), acoustic, and vision-based approaches, and examine their distinct advantages and limitations. The research also discusses the importance of sensor fusion methods and other detection approaches, including wireless fidelity (Wi-Fi), cellular, and Internet of Things (IoT) networks, for improving the accuracy and efficiency of UAV detection and identification.

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UAV Fast Signal Detection Algorithm with Convolutional Neural Network

Lian

Liu

et al. 2022

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

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CNN-based UAV Detection with Short Time Fourier Transformed Acoustic Features

Cited by 4 publications

References 4 publications

Deep learning‐based unmanned aerial vehicle detection in the low altitude clutter background

Deep learning‐based unmanned aerial vehicle detection in the low altitude clutter background

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

UAV Fast Signal Detection Algorithm with Convolutional Neural Network

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