Multistatic Radar Measurements of UAVs at X-band and L-band

Beasley, Piers J.; Ritchie, Matthew; Griffiths, Hugh; Miceli, William J.; Inggs, Michael; Lewis, Simon; Kahn, Bradley

doi:10.1109/radarconf2043947.2020.9266444

Cited by 23 publications

(13 citation statements)

References 12 publications

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“…A simulator is also built based on the system's capabilities. In [262], a NeXtRAD radar system is used for simultaneous monostatic and bi-static measurements. The measurements were carried out at X-band and L-bands.…”

Section: E Radars Using Popular Frequency Bandsmentioning

confidence: 99%

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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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Section: E Radars Using Popular Frequency Bandsmentioning

confidence: 99%

“…The measurements were carried out at X-band and L-bands. In [262], it is shown that the NeXtRAD radar system is capable of detecting multiple small UAVs. A holographic 3D radar capable of wide-area multi-beam surveillance for detection of micro UAVs is provided in [263].…”

Section: E Radars Using Popular Frequency Bandsmentioning

confidence: 99%

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

Khawaja¹

2022

Preprint

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“…This system has been used to capture measurement of sea clutter, drones and small boat targets while based in the Simon's Town region in South Africa [30]- [35]. These two radar systems are almost unique in their position as coherent multistatic radars that are capable of networked sensing of targets using GPSDO synchronisation [29].…”

Section: Nextradmentioning

confidence: 99%

Radar UAV and bird signature comparisons with micro-Doppler

Ritchie

Horne

Peters

2021

Radar Countermeasures for Unmanned Aerial Vehicles

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This chapter reviews the similarities and differences between micro Unmanned Aerial Vehicles (UAVs), also referred to as drones, and bird targets from the signals they present to radar sensors. With the increasing usage of UAV platforms in both military and civilian applications, the demand for the ability to sense drone locations and discriminate them from background clutter and non-drone targets is becoming a vital requirement. A comparable target in size, speed and Radar Cross Section (RCS) is a bird. These are present almost everywhere that radar systems have to operate and have been detected by radar since the early origin of radar engineering. Due to the similarity in radar signature birds can cause common misclassification between them and the priority drone targets which has been identified as a current key challenge in radar sensing. In this chapter radar bird and drone signature research is initially summarised, then a fundamental model that represents the key contributions from drone rotor blades is introduced and compared to real measurements. Laboratory measurements of quadcopter rotor blade signatures with across 4 linear polarisations are then investigated in order to evaluate the trend of Signal-to-Noise-Ratio (SNR) vs. aspect angle. Next bird signatures from two separate radar systems are shown and compared to drone targets also present in the captures which are of comparable size and RCS. The outputs of all research presented are then summarised in the concluding remarks.

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“…In order to reduce the false alarm rate and thus mature radar based drone detection technology, there is a widening body of research into methods for discriminating between birds and micro-drones. The micro-Doppler signatures of drones have been identified as one method for discriminating micro-drones from other targets and general clutter (Tahmoush, 2014;Ritchie et al, 2016;Samiur and Robertson, 2019;Beasley et al, 2020). Micro-Doppler signatures are produced by periodic micro-motions of a target or object, such as flapping of a bird's wings, the gait of a human walk or rotation of a micro-drone's propellers (Chen, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Further details on the mechanisms that produce micro-Doppler have been extensively documented for a range of radar targets in Miceli (2014) and Chen (2019). There are several publications detailing micro-Doppler signatures of drones, where comparisons are made with bird micro-Doppler e.g., Tahmoush (2014), Ritchie et al (2016), Rahman and Robertson (2018), and Beasley et al (2020). This work has been taken further in Molchanov et al (2014), Kim et al (2017), Samiur and Robertson (2019), and , where drone micro-Doppler signatures are fed into classifiers in order to discriminate between both different classes of drones and between drones and birds.…”

Section: Introductionmentioning

confidence: 99%

Multi-Frequency Radar Micro-Doppler Based Classification of Micro-Drone Payload Weight

Dhulashia

Peters

Horne

et al. 2021

Front. Signal Process.

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The use of drones for recreational, commercial and military purposes has seen a rapid increase in recent years. The ability of counter-drone detection systems to sense whether a drone is carrying a payload is of strategic importance as this can help determine the potential threat level posed by a detected drone. This paper presents the use of micro-Doppler signatures collected using radar systems operating at three different frequency bands for the classification of carried payload of two different micro-drones performing two different motions. Use of a KNN classifier with six features extracted from micro-Doppler signatures enabled mean payload classification accuracies of 80.95, 72.50 and 86.05%, for data collected at S-band, C-band and W-band, respectively, when the drone type and motion type are unknown. The impact on classification performance of different amounts of situational information is also evaluated in this paper.

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Multistatic Radar Measurements of UAVs at X-band and L-band

Cited by 23 publications

References 12 publications

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

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

Radar UAV and bird signature comparisons with micro-Doppler

Multi-Frequency Radar Micro-Doppler Based Classification of Micro-Drone Payload Weight

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