Doppler characteristics of micro-drones with L-Band multibeam staring radar

Jahangir, Mohammed; Baker, Chris; Oswald, G.A.

doi:10.1109/radar.2017.7944360

Cited by 45 publications

(23 citation statements)

References 5 publications

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“…Compared with optical and infrared detection, radar can extend observational capabilities to aroundthe-clock operations and expand spatial coverage in both distance and altitude, despite interference by bad weather such as rain, fog, and darkness, when vision is impaired [5]. Air surveillance radars (ASRs) are not fit for detecting drones at low altitude because the mission of ASR focuses on searching for aircraft that have a large radar cross section (RCS) in the sky, not at low altitude [6,7]. Besides, detection of drones with high range resolution radar profiles (HRRPs) is problematic because subcentimeter resolution is needed to capture the longitudinal structure of targets less than 100 cm in length [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…A typical customer drone is DJI Phantom. It weighs several kg and a top speed of 16 m/s [6]. Despite the fact that the maximum flight height of a drone is 6000 m, the general altitude at which drones are allowed to fly is several hundreds of meters [4].…”

Section: Introductionmentioning

confidence: 99%

“…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]. When collecting radar data from a drone with L-band multibeam staring radar, Jahangir et al usually obtain unexpected radar echoes that almost certainly are birds in multiple scenarios [6]. Obviously, birds can jam the detection of 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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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

View full text Add to dashboard Cite

show abstract

“…A recent study showed that these signals can be analyzed in a time‐frequency plot, namely a micro‐Doppler signature or a spectrogram. Additionally, a study based on the micro‐Doppler signature produced an efficient method based on the micro‐Doppler signature to distinguish and classify targets.…”

Section: Introductionmentioning

confidence: 99%

Drone Detection with Chirp-Pulse Radar Based on Target Fluctuation Models

Kim¹,

Park²,

Park³

et al. 2018

ETRI Journal

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This paper presents a pulse radar system to detect drones based on a target fluctuation model, specifically the Swerling target model. Because drones are small atypical objects and are mainly composed of non‐conducting materials, their radar cross‐section value is low and fluctuating. Therefore, determining the target fluctuation model and applying a proper integration method are important. The proposed system is herein experimentally verified and the results are discussed. A prototype design of the pulse radar system is based on radar equations. It adopts three different pulse modes and a coherent pulse integration to ensure a high signal‐to‐noise ratio. Outdoor measurements are performed with a prototype radar system to detect Doppler frequencies from both the drone frame and blades. The results indicate that the drone frame and blades are detected within an instrumental maximum range. Additionally, the results show that the drone's frame and blades are close to the Swerling 3 and 4 target models, respectively. By the analysis of the Swerling target models, proper integration methods for detecting drones are verified and can thus contribute to increasing in detectability.

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“…The radar provides a significant amount of information on the object and so can create a signature that can be characterised and identified. As the sensor is able to continuously stare at the target, the Doppler resolution is very high and can be used to measure and exploit the target micro-Doppler signature characteristics [1]. For applications of Air Traffic Control (ATC), the continuous dwell on the target and high Doppler resolution are exploited to fine tune post detection filter processes that reject false alarms and preserve only detections from genuine aircraft [2].…”

Section: Introductionmentioning

confidence: 99%

Simulations of L-band Staring Radar Moving Target Integration Efficiency

Gersone

Balleri

Baker³

et al. 2018

2018 IEEE Conference on Antenna Measurements &Amp; Applications (CAMA)

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Aveillant Ltd has developed a staring L-band radar that deploys a static quasi-monostatic antenna in transmission and a static digital phased array on receive capable of generating multiple simultaneous beams. Because the antenna is not rotating, the radar can stare at targets and select long dwell times with no effect on the scan rate. High Doppler resolution can be achieved and used to detect small targets, such as drones, even in heavy clutter. Despite the staring array, targets moving with a variable radial velocity generate echoes with a timevarying Doppler frequency shift that limits the integration gain achievable with standard Fourier Transform based techniques. As a result, the number of pulses can be integrated remains limited to the effective coherent processing interval with a consequent suboptimal Signal to Noise Ratio (SNR). This paper presents the results of a set of simulations aimed at studying the integration gain efficiency of a staring radar of the type of the Aveillant Holographic radar for targets moving with a constant and non-constant radial velocity. The case of a target flying horizontally with respect to the radar boresight is investigated to show that compensation techniques can be potentially employed to maximise coherence on the target and the resulting integration gain.

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Doppler characteristics of micro-drones with L-Band multibeam staring radar

Cited by 45 publications

References 5 publications

Interference of Radar Detection of Drones by Birds

Interference of Radar Detection of Drones by Birds

Drone Detection with Chirp-Pulse Radar Based on Target Fluctuation Models

Simulations of L-band Staring Radar Moving Target Integration Efficiency

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