Micro-UAV Detection with a Low-Grazing Angle Millimeter Wave Radar

Ezuma, Martins; Özdemir, Özgür; Anjinappa, Chethan Kumar; Gulzar, Wahab Ali; Güvenç, İsmail

doi:10.1109/rws.2019.8714203

Cited by 43 publications

(29 citation statements)

References 7 publications

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“…In this way, it has aroused interest in venturing into UAV detection and tracking techniques, but there is a problem in suppressing clutter since these vehicles are influenced by false alerts. Likewise, the tracking of trajectories is interrupted by objects or vehicles that are at the same height [5], [6]. Unlike active radar, passive radar uses transmitters found on FM, TV, or mobile phone frequencies.…”

Section: Passive Radar With Coherent Dual Channelmentioning

confidence: 99%

Design of a Passive Radar based on RTL-SDR technology with coherent dual channel

Mediavilla

Espinoza

Espinosa

et al. 2022

J. Phys.: Conf. Ser.

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In this research, a passive radar aimed at the detection of unmanned aerial vehicles was developed. As a UAV navigates at low altitudes and high speeds, RTL-SDR modules are used that with the help of two antennas, one directional and one reference, the position of the object is detected through bounce and signal discrimination, for which several tests were carried out in based on frequencies that are in the FM range. For this, GNU radio is used and data processing with Python in the Anaconda guide that facilitates obtaining data visually through a video of the frequency of the Doppler effect vs the distance in kilometers. Also, mention is made of the synchronization arrangement that is established between the RTL-SDR modules based on their oscillator.

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Section: Passive Radar With Coherent Dual Channelmentioning

confidence: 99%

Design of a Passive Radar based on RTL-SDR technology with coherent dual channel

Mediavilla

Espinoza

Espinosa

et al. 2022

J. Phys.: Conf. Ser.

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“…In [ 66 ] the detection of UAVs is addressed in the context of non-Rayleigh clutter, specifically, distributed as a Weibull. By applying a detector based on CA-CFAR, the presence of the target is detected and then identified in range and speed through the 2D FFT map.…”

Section: Literature On Drone Detectionmentioning

confidence: 99%

Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review

Coluccia

Parisi

Fascista

2020

Sensors

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Thanks to recent technological advances, a new generation of low-cost, small, unmanned aerial vehicles (UAVs) is available. Small UAVs, often called drones, are enabling unprecedented applications but, at the same time, new threats are arising linked to their possible misuse (e.g., drug smuggling, terrorist attacks, espionage). In this paper, the main challenges related to the problem of drone identification are discussed, which include detection, possible verification, and classification. An overview of the most relevant technologies is provided, which in modern surveillance systems are composed into a network of spatially-distributed sensors to ensure full coverage of the monitored area. More specifically, the main focus is on the frequency modulated continuous wave (FMCW) radar sensor, which is a key technology also due to its low cost and capability to work at relatively long distances, as well as strong robustness to illumination and weather conditions. This paper provides a review of the existing literature on the most promising approaches adopted in the different phases of the identification process, i.e., detection of the possible presence of drones, target verification, and classification.

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“…In [18], the micro-Doppler signature of a quad-copter is compared with that of a walking human. The study concludes that the unique micro-Doppler signature of the micro-UAVs are useful for the design of an automatic target recognition (ATR) system.…”

Section: A Radar-based Techniquesmentioning

confidence: 99%

Micro-UAV Detection and Classification from RF Fingerprints Using Machine Learning Techniques

Ezuma

Erden

Anjinappa

et al. 2019

2019 IEEE Aerospace Conference

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160

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This paper focuses on the detection and classification of micro-unmanned aerial vehicles (UAVs) using radio frequency (RF) fingerprints of the signals transmitted from the controller to the micro-UAV. In the detection phase, raw signals are split into frames and transformed into the wavelet domain to remove the bias in the signals and reduce the size of data to be processed. A naive Bayes approach, which is based on Markov models generated separately for UAV and non-UAV classes, is used to check for the presence of a UAV in each frame. In the classification phase, unlike the traditional approaches that rely solely on time-domain signals and corresponding features, the proposed technique uses the energy transient signal. This approach is more robust to noise and can cope with different modulation techniques. First, the normalized energy trajectory is generated from the energy-time-frequency distribution of the raw control signal. Next, the start and end points of the energy transient are detected by searching for the most abrupt changes in the mean of the energy trajectory. Then, a set of statistical features is extracted from the energy transient. Significant features are selected by performing neighborhood component analysis (NCA) to keep the computational cost of the algorithm low. Finally, selected features are fed to several machine learning algorithms for classification. The algorithms are evaluated experimentally using a database containing 100 RF signals from each of 14 different UAV controllers. The signals are recorded wirelessly using a high-frequency oscilloscope. The data set is randomly partitioned into training and test sets for validation with the ratio 4:1. Ten Monte Carlo simulations are run and results are averaged to assess the performance of the methods. All the micro-UAVs are detected correctly and an average accuracy of 96.3% is achieved using the k-nearest neighbor (kNN) classification. Proposed methods are also tested for different signal-to-noise ratio (SNR) levels and results are reported.

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Micro-UAV Detection with a Low-Grazing Angle Millimeter Wave Radar

Cited by 43 publications

References 7 publications

Design of a Passive Radar based on RTL-SDR technology with coherent dual channel

Design of a Passive Radar based on RTL-SDR technology with coherent dual channel

Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review

Micro-UAV Detection and Classification from RF Fingerprints Using Machine Learning Techniques

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