2022
DOI: 10.1109/jsen.2021.3105229
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UAV Detection and Localization Based on Multi-Dimensional Signal Features

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Cited by 37 publications
(16 citation statements)
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“…The features set is reduced using Neighborhood component analysis (NCA) to improve the model accuracy and robustness. A Wi-Fi based drone detection and identification system leveraging ML is proposed in [61]. The RF sensor monitor the channel to capture the Channel State Information (CSI) information.…”
Section: B Rf-based Detectionmentioning
confidence: 99%
See 1 more Smart Citation
“…The features set is reduced using Neighborhood component analysis (NCA) to improve the model accuracy and robustness. A Wi-Fi based drone detection and identification system leveraging ML is proposed in [61]. The RF sensor monitor the channel to capture the Channel State Information (CSI) information.…”
Section: B Rf-based Detectionmentioning
confidence: 99%
“…ML techniques including SVM, Random Forest (RF) [62], Naive Bayes (NB), Ensemble Learning (EL), and KNN are then used to detect the drone. The authors in [61] also proposed in [63] to extract other features from CSI including Fractal Dimension (FD), Axially Integrated Bispectra (AIB), and Square Integrated Bispectra (SIB) and then applied PCA [64] and NCA [65] methods to reduce the feature dimensionality and trained ML classifiers (SVM, KNN) to perform the detection task. The aforementioned works used traditional ML algorithms, which typically does not provide sufficient accuracy and robust performance in practical large deployments due to the non-linearity in the RF signals.…”
Section: B Rf-based Detectionmentioning
confidence: 99%
“…Most UAs used nowadays have an on-board Inertial Measurement Unit (IMU), and a source of position sensing, such as Real-Time Kinematics (RTK) systems [2], Motion Capture (MoCap) [3], Radio Frequency (RF) [4], [5], electromagnetic [6], and Ultra Wide Bandwidth (UWB) [7]), in addition to on-board measurements (e.g. visual odometry [8], [9]).…”
Section: A Relevant Workmentioning
confidence: 99%
“…The energy spectrum of the radio emissions using a multistage detector is used to detect and classify UAVs in the presence of noise and interference from other communication nodes [295]. In [296], multi-dimensional signal features are used for the Fig. 20: A laser mesh created in the air using two airborne UAVs for detection, tracking, and classification of aerial vehicles (regenerated from [297]).…”
Section: Radio Frequency Analyzersmentioning
confidence: 99%