Micro-Doppler extraction of a small UAV in a non-line-of-sight urban scenario

Gustavsson, Magnus; Andersson, Åsa Scott; Johansson, Tommy; Jönsson, Robert; Karlsson, Nadine; Nilsson, S.

doi:10.1117/12.2260709

Cited by 10 publications

(9 citation statements)

References 7 publications

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“…It is capable of obtaining large time-bandwidth results, achieving long detection ranges and medium-range resolution simultaneously with linear frequency modulation technology. The working band is Ku (12)(13)(14)(15)(16)(17)(18), and the typical wavelength is 2 cm. The range resolution is 15 m. Its peak power is 384 W when all T/R subunits are working.…”

Section: Methodsmentioning

confidence: 99%

“…The researches on the differentiation between flying birds and small drones by radar are relatively new. Yet, the common view is that birds become the major jamming of radar detecting drones because of the similarity in RCS [14,15], motion pattern [9,16,17], and even similar micro-Doppler features [18]. Moreover, Ritchie et al reported that various birds will interfere with micro-drones in comparable signature within the time domain and similar RCS values, and the discrimination between birds and drones is needed to avoid significant false alarm rates [19].…”

Section: Introductionmentioning

confidence: 99%

“…Obviously, birds can jam the detection of drones. Many researches have involved discussion about classification between birds and drones [8,15,19,20]. Yet, several questions remain unanswered before we investigate the potential methods of classification between birds and drones.…”

Section: Introductionmentioning

confidence: 99%

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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: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Interference of Radar Detection of Drones by Birds

Gong¹,

Yan²,

Li³

et al. 2019

PIER M

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“…There have been many works done in exploiting NLOS multipaths to detect hidden targets [10]- [24]. In [10]- [13], the multipath returns received by the X-band radar were utilized to obtain the micro-Doppler features from moving targets. In [14], Zetik et al proposed a one-dimensional target localization method based on a UWB sensor operated in the frequency band from C-band to X-band.…”

Section: Introductionmentioning

confidence: 99%

NLOS Target Localization Behind an L-Shaped Corner With an L-Band UWB Radar

et al. 2020

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For the reason of target multipaths ambiguities, localization for hidden moving target due to lack of line of sight (LOS) path in urban environments is a hard task. To handle this problem we propose a localization algorithm based on the internal connection of these multipaths in this paper. First, we establish an electromagnetic (EM) propagation model based on the characters of the measured signal received by an L-band single-input single-output (SISO) ultra-wideband (UWB) radar. After that, the model-based target localization algorithm is proposed based on a times-of-arrival (TOAs) association method, which is able to match the measured TOAs with the corresponding EM propagation paths correctly. The proposed method can cope with the case while some missing detections of TOAs happen. Simulation and experimental results ultimately validate the robustness and the state-of-the-art performance of the proposed algorithm. INDEX TERMS NLOS target localization, corner environment, UWB radar, multipath exploitation. GUOLONG CUI (Senior Member, IEEE) received the B.S. degree in electronic information engineering and the M.S. and Ph.D. degrees in signal and information processing from the

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“…In turn, the identification of UAV at long distance is the proposal presented in [13]. Doppler effect estimates the number of revolutions per minute of the drone rotors, and the Plotted Image Machine Learning (PIL) and K-Nearest Neighbors (KNN) classify them, based on average distance similarity of a preset signature data-base.…”

Section: Spectrogram Approach: State Of the Artmentioning

confidence: 99%

Spectrogram Analysis: An Approach to Identify The Quadrotor Thrust Model

Brandão¹,

Pizetta²,

Pizziolo³

et al. 2018

Preprint

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Abstract:This paper deals with a non-contact method to identify the aerodynamic propeller parameters of the Parrot AR.Drone quadrotor. The experimental set consists in a camera recording the vehicle flights, the audio signal is extracted and is used a spectrogram analysis to estimates the propeller velocity. First, the aerial vehicle takes off and starts a hovering maneuver. The experiment is repeated with different additional masses attached to its rigid body. If the weight over the UAV increases/decreases, then the propeller must rotates faster/slower to produce a higher/lower thrust, and consequently, the sound frequency increases/decreases. Finally, this proposition is validated experimentally, and the estimated velocity is used to identify the quadrotor thrust parameters.

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Micro-Doppler extraction of a small UAV in a non-line-of-sight urban scenario

Cited by 10 publications

References 7 publications

Interference of Radar Detection of Drones by Birds

Interference of Radar Detection of Drones by Birds

NLOS Target Localization Behind an L-Shaped Corner With an L-Band UWB Radar

Spectrogram Analysis: An Approach to Identify The Quadrotor Thrust Model

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