Drone classification using RF signal based spectral features

Kılıç, Rabiye; Kumbasar, Nida; Oral, Emin Argun; Özbek, İ. Yücel

doi:10.1016/j.jestch.2021.06.008

Cited by 25 publications

(16 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The XGBoost models achieve average accuracies (using cross validation techniques) of 99.96%, 90.73% and 70.09%, for two, four and ten classes, respectively. Kılıç et al [31] demonstrate the similarity between RF and audio signals in terms of time-and frequency-dependent characteristics. The proposed approach utilizes wellestablished spectral-based audio features like PSD, MFCC, and linear frequency cepstrum coefficients (LFCC) within a Support Vector Machine (SVM)-based machine learning framework.…”

Section: Rf-based Classificationmentioning

confidence: 99%

“…Similar works based on the radar for drone classification in different radar systems (1)(2)(3)(4) GHz) can achieve a high accuracy of 95-100% using machine learning methods [22][23][24]. Currently, the leading drone detection techniques are based on either RF or acoustic signals [15,[27][28][29][30][31][32][33][34][35][36][37]. Therefore, the following literature review focuses on related work that is based on these two approaches.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Drones Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks

Frid,

Ben-Shimol,

Manor

et al. 2024

Sensors

View full text Add to dashboard Cite

The use of drones has recently gained popularity in a diverse range of applications, such as aerial photography, agriculture, search and rescue operations, the entertainment industry, and more. However, misuse of drone technology can potentially lead to military threats, terrorist acts, as well as privacy and safety breaches. This emphasizes the need for effective and fast remote detection of potentially threatening drones. In this study, we propose a novel approach for automatic drone detection utilizing the usage of both radio frequency communication signals and acoustic signals derived from UAV rotor sounds. In particular, we propose the use of classical and deep machine-learning techniques and the fusion of RF and acoustic features for efficient and accurate drone classification. Distinct types of ML-based classifiers have been examined, including CNN- and RNN-based networks and the classical SVM method. The proposed approach has been evaluated with both frequency and audio features using common drone datasets, demonstrating better accuracy than existing state-of-the-art methods, especially in low SNR scenarios. The results presented in this paper show a classification accuracy of approximately 91% at an SNR ratio of −10 dB using the LSTM network and fused features.

show abstract

Section: Rf-based Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Drones Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks

Frid,

Ben-Shimol,

Manor

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Advanced hardware with excellent accelerated abilities and deep learning technologies have made accurate and robust UAV detection possible. Convolution neural network (CNN)the most basic deep learning model classifies UAVs by using visual and acoustic information [11], [13] and have improved more feature extraction technique than traditional object recognition algorithms. Complex deep learning models, such as YOLO (you only look once) [13], have excellent object recognition precision and speed compared to the basic models.…”

Section: A Challenges and Limitationsmentioning

confidence: 99%

“…The deployed system targeted the influence of multiple sensors on 3D indoor location accuracy when faced with different-sized UAVs. In [11], the authors adopted RF signals and spectral-based audio characteristics with SVM for drone identification and classification. They adjusted the spectral feature parameters for drone signal categorization.…”

Section: B Contributionsmentioning

confidence: 99%

“…According to Lykou et al in [5], around 6% commercial UAV detection systems designed on acoustic sensors, 26% are using radio frequency (RF), 28% are radar-based, and 40% adopted visual sensors for detection. Mostly those sensors adopted for drone detection and classification are: radar (radio detection and ranging on several different frequency bands for both active and passive sensing) [6], cameras for the visible spectrum sensing [4], cameras detecting thermal or infrared (IR) emissions [7], LIDAR (light detection and ranging) [8], microphones/acoustic sensors for acoustic vibration detection [2], and RF [9], [10] monitoring sensors for the detection of radio signals [11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network

dil¹,

khan²,

Alam³

et al. 2022

Preprint

View full text Add to dashboard Cite

<p>Unmanned air vehicles (UAVs) popularity is on the rise as it enables the services like traffic monitoring, emergency communications, deliveries, and surveillance. However, the unauthorized usage of UAVs (a.k.a drone) may violate security and privacy protocols for security-sensitive national and international institutions. The presented challenges require fast, efficient, and precise detection of UAVs irrespective of harsh weather conditions, the presence of different objects, and their size to enable SafeSpace. Recently, there has been significant progress in using the latest deep learning models, but those models have shortcomings in terms of computational complexity, precision, and non-scalability. To overcome these limitations, we propose a precise and efficient multiscale and multifeature UAV detection network for SafeSpace, i.e., \textit{MultiFeatureNet} (\textit{MFNet}), an improved version of the popular object detection algorithm YOLOv5s. In \textit{MFNet}, we perform multiple changes in the backbone and neck of the YOLOv5s network to focus on the various small and ignored features required for accurate and fast UAV detection. To further improve the accuracy and focus on the specific situation and multiscale UAVs, we classify the \textit{MFNet} into small (S), medium (M), and large (L): these are the combinations of various size filters in the convolution and the bottleneckCSP layers, reside in the backbone and neck of the architecture. This classification helps to overcome the computational cost by training the model on a specific feature map rather than all the features. The results show significant performance gain even for unseen feature maps with minimal loss in accuracy. Results show a significant reduction in training parameters, inference, and increased pattern in FPS and GFLOPs for \textit{MFNet} compared to YOLOv5s. \textit{MFNet-M} performance evaluation in terms of precision, recall, mean average-precision (mAP), and IOU increased around 1.8\%, 2.2\%, 0.9\%, 1.7\% compared to YOLOv5s. Furthermore, \textit{MFNet-M} achieves the best performance with 96.8\% precision, 88.4\% recall, 95.9\% mAP, and 51.1\% IoU for UAV detection. The dataset and code are available as an open source: github.com/ZeeshanKaleem/MultiFeatureNet.</p>

show abstract

Accelerometer Signal Classification Algorithms Based on Mel-Frequency Cepstrum and Fully Connected Neural Network

Sukharev

Sukharev²

2022

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Drone classification using RF signal based spectral features

Cited by 25 publications

References 13 publications

Drones Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks

Drones Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks

SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network

Accelerometer Signal Classification Algorithms Based on Mel-Frequency Cepstrum and Fully Connected Neural Network

Contact Info

Product

Resources

About