An Investigation on the Radar Signatures of Small Consumer Drones

Li, Chenchen J.; Ling, Hao

doi:10.1109/lawp.2016.2594766

Cited by 79 publications

(46 citation statements)

References 7 publications

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“…Some studies claim that wings have a negligible effect on bird echo intensity [29], while others attempt to extract amplitude modulation envelope signatures [28] correlated with the flapping pattern of wings [30] to classify bird species [26,27]. Some investigations believe that the plastic drone blades do not contribute to a significant return [17,43], but others report that blades give rise to "blade flash" [41], which is a term given to the short impulse of high radar return that is observed in blade signature [16]. Still, there are no visual clues of micro-Doppler for human operators in our Ku-band radar data.…”

Section: Discussionmentioning

confidence: 99%

Interference of Radar Detection of Drones by Birds

Gong¹,

Yan²,

Li³

et al. 2019

PIER M

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Recently, consumer drones have encroached upon airports and pose a potential threat to aviation safety. Radar is an effective remote sensing tool to detect and track flying drones. Radar echoes from flying birds are assumed to be clutters when a radar is detecting drones. Yet, few studies have reported how radar echoes from flying birds interfere with the detection of drones, how similar radar cross section (RCS) and flight feature of birds and drones are, and why the flying birds cause trouble when radar identifies signals from the drone. In this study, we collected 3900 × 256 of Ku-band radar echoes of flying birds and consumer drones. The targets consist of a pigeon, a crane, waterfowl, and a DJI Phantom 3 Vision drone. We compared the maximum detectable range of birds and drones, the time series and the Doppler spectrum of radar echoes from the birds and the drone, considering oncoming and outgoing radar data with respect to radar location. The statistical results indicate that flying birds have similar RCS, same velocity range, similar signal fluctuation, and approximate signal amplitude. Our results of radar automatic target recognition (ATR) illuminate that the identification probability of airborne drones will be lower due to the interference of the radar signal by flying birds. Above all, these facts confirm that flying birds are the main cause of interference when a radar is detecting and identifying airborne drones.

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Section: Discussionmentioning

confidence: 99%

Interference of Radar Detection of Drones by Birds

Gong¹,

Yan²,

Li³

et al. 2019

PIER M

View full text Add to dashboard Cite

show abstract

“…Therefore, in order to evade detection, military aircrafts are coated with radiation-absorbent materials (RAM) to reduce the RCS. On the other hand, experimental measurements at millimeter wave frequencies show that many commercially available micro-UAVs have an average RCS value of about 0.02 m 2 (-16.98 dBsm) [26]- [28]. This very low RCS value is due to the shape and design material of these micro-UAVs.…”

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

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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“…Sensing modalities that can be used to solve drone detection problem are typically RADAR [4], acoustics [5], visual [6], IR [7] (thermal and short-wave), sensing of the radio spectrum [8], LIDAR [9], etc. However, as the problem is so difficult to solve in realistic operating conditions, most of the existing solutions rely on a mix of different sensing methodologies in order to solve the drone detection problem [1] and use a mix of traditional detection and tracking methodologies [10], [11] originating from computer vision to achieve multi-sensor tracking.…”

Section: Previous Work On Drone Detection and The Scope Of The Safeshmentioning

confidence: 99%

Using a qualitative and quantitative validation methodology to evaluate a drone detection system

Doroftei

Cubber

2019

ACTA IMEKO

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Now that the use of drones is becoming more common, the need to regulate the access to airspace for these systems is becoming more pressing. A necessary tool in order to do this is a means of detecting drones. Numerous parties have started the development of such drone detection systems. A big problem with these systems is that the evaluation of the performance of drone detection systems is a difficult operation that requires the careful consideration of all technical and non-technical aspects of the system under test. Indeed, weather conditions and small variations in the appearance of the targets can have a huge difference on the performance of the systems. In order to provide a fair evaluation, it is therefore paramount that a validation procedure that finds a compromise between the requirements of end users (who want tests to be performed in operational conditions) and platform developers (who want statistically relevant tests) is followed. Therefore, we propose in this article a qualitative and quantitative validation methodology for drone detection systems. The proposed validation methodology seeks to find this compromise between operationally relevant benchmarking (by providing qualitative benchmarking under varying environmental conditions) and statistically relevant evaluation (by providing quantitative score sheets under strictly described conditions).

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An Investigation on the Radar Signatures of Small Consumer Drones

Cited by 79 publications

References 7 publications

Interference of Radar Detection of Drones by Birds

Interference of Radar Detection of Drones by Birds

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

Using a qualitative and quantitative validation methodology to evaluate a drone detection system

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