Measurements of Birds and Drones with L-Band Staring Radar

Jahangtr, M.; Atkinson, George; Antoniou, Michail; Baker, C.J.; Sadler, John; Reynolds, Sarah J.

doi:10.23919/irs51887.2021.9466224

Cited by 15 publications

(9 citation statements)

References 12 publications

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“…The top one is of a single bird showing an absence of any visible micro-Doppler that may be a reliable distinguishing feature from drone signatures with HERM lines (Figure 10). The bottom plot is from Another opportunity the permanent radar installation offers is observing bird activity over long periods to map bird locations and movements within a dense urban setting [9]. Figure 15 compares heatmaps for two periods of the day between 04.00 and 08.00 h (GMT), and 20.00 and 23.59 h (GMT) on 10/02/2022.…”

Section: Opportune Bird Characteristicsmentioning

confidence: 99%

“…Captive birds can be flown with miniaturised GPS tags to obtain good truth data which enable detailed analysis of the radar signatures from birds, in a similar manner to that used for drone targets. Previously, a Microsensory GPS tag, pictured in Figure 7a, was used to obtain truth data for captive raptors during radar trials conducted at a rural location [9]. A GPS tag was supplied by PathTrack Ltd (Otley, UK), weighed less than 5 g, measured 31 (L) × 14 (W) × 10 (D) mm (Figure 7b) and can record GPS data for an hour in tracking racing pigeons ( Columba livia ) as control birds to collect radar data.…”

Section: Field Trials Proceduresmentioning

confidence: 99%

“…Another opportunity the permanent radar installation offers is observing bird activity over long periods to map bird locations and movements within a dense urban setting [9]. Figure 15 compares heatmaps for two periods of the day between 04.00 and 08.00 h (GMT), and 20.00 and 23.59 h (GMT) on 10/02/2022.…”

Section: Processing Capabilitiesmentioning

confidence: 99%

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Development of a networked photonic‐enabled staring radar testbed for urban surveillance

Jahangir,

Griffiths,

White

et al. 2023

IET Radar Sonar & Navi

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Urban surveillance of slow‐moving small targets such as drones and birds in low to medium airspace using radar presents significant challenges. Detecting, locating and identifying such low observable targets in strong clutter requires both innovation in radar hardware design and optimisation of processing algorithms. To this end, the University of Birmingham (UoB) has set‐up a testbed of two L‐band staring radars to support performance benchmarking using datasets of target and clutter from realistic urban environment. This testbed is also providing the vehicle to understand how novel radar architectures can enhance radar capabilities. Some of the challenges in installing the radar at the UoB campus are highligted. Detailed benchmarking results are provided from urban monostatic and bistatic field trials that form the basis for performance comparison against future hardware modification. The solution to the challenge of interfacing the radar to the external oscillators is described and stand‐alone bench tests with the candidate oscillators are reported. The testbed provides a valuable capability to undertake detailed analysis of performance of Quantum photonic‐enabled radar and allows for its comparison with conventional oscillator technology for surveillance of low observable targets in the presence of urban clutter.

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Section: Opportune Bird Characteristicsmentioning

confidence: 99%

Section: Field Trials Proceduresmentioning

confidence: 99%

See 1 more Smart Citation

Development of a networked photonic‐enabled staring radar testbed for urban surveillance

Jahangir,

Griffiths,

White

et al. 2023

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

“…The radar system used in [180] is a bird surveillance radar system installed at airports and operates at S-band. The measurements for the classification of UAVs and birds using L-band staring radar are provided in [181] in an urban area.…”

Section: Classification Of Aerial Vehicles Using Radar Systemsmentioning

confidence: 99%

A Survey on Radar Techniques for Detection,Tracking, and Classification of Aerial Threats

Khawaja¹

2022

Preprint

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<p> The use of unmanned aerial vehicles (UAVs) for different applications has increased many folds in recent years. The UAVs are expected to change the future air operations. However, there are instances where the UAVs can be used for malicious purposes. The detection, tracking, and classification of UAVs is challenging compared to manned aerial vehicles (MAVs) mainly due to small size, complex shapes, and ability to fly close to the terrain and in autonomous flight patterns in swarms. In this survey, we will discuss current and future aerial threats, and provide an overview of radar systems to counter such threats. We also study the performance parameters of radar systems for the detection, tracking, and classification of UAVs compared to MAVs. Finally, limitations of radar systems and comparison with other techniques that do not rely on radars for detection, tracking, and classification of aerial threats are also provided. </p>

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“…Therefore, it is an important task to find new approaches to solve the classification problem when the micro-Doppler method fails. In [7], feature extraction from flight trajectory is utilized to distinguish drones from birds and other aircraft, which provides some new thoughts for classification, instead of being limited to micro-Doppler methods.…”

mentioning

confidence: 99%

A novel feature extraction method of human‐vehicle classification based on low resolution radar

Chen

2022

Electronics Letters

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In this paper, a novel method using target movement characteristic is proposed to classify human and vehicle based on low resolution radar system. Many studies have demonstrated the ability of radar micro‐Doppler signature for ground moving targets classification. However, the micro‐Doppler signals are not always observed in practical application. Considering the motion characteristic of human and vehicle, a multi‐frame joint scheme is proposed, in which three suitable features are extracted and the Random Forest classifier is used to identify three kinds of moving targets. The experiment results with satisfied classification accuracy prove that the proposed method is capable of human‐vehicle classification.

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Measurements of Birds and Drones with L-Band Staring Radar

Cited by 15 publications

References 12 publications

Development of a networked photonic‐enabled staring radar testbed for urban surveillance

Development of a networked photonic‐enabled staring radar testbed for urban surveillance

A Survey on Radar Techniques for Detection,Tracking, and Classification of Aerial Threats

A novel feature extraction method of human‐vehicle classification based on low resolution radar

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