Measurements of Multistatic X&amp;L Band Radar Signatures of UAVS

Palamà, Riccardo; Fioranelli, Francesco; Ritchie, Matthew; Inggs,; Lewis, Simon; Griffiths, Hugh

doi:10.1109/radar41533.2019.171389

Cited by 7 publications

(5 citation statements)

References 10 publications

(8 reference statements)

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“…In recent years, many research teams have collected and analyzed the real data of birds and drones using various radar devices [23][24][25][26][27]. Micro-Doppler signatures have been studied for target recognition.…”

Section: Target-dependent False Alarmsmentioning

confidence: 99%

A Gaussian Mixture CPHD Filter for Multi-Target Tracking in Target-Dependent False Alarms

Jiang,

Wang,

Dou

et al. 2024

Remote Sensing

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The estimation of the target number and individual tracks are two major tasks in multi-target tracking. The main shortcoming of traditional tracking methods is the cumbersome data association between measurements and targets. The cardinalized probability hypothesis density filter (CPHD) proposed in recent years can achieve the requirement for multitarget tracking. This kind of filter jointly estimates the cardinality distribution and the posterior density, which can achieve a more stable estimate of the target number. However, targets with complex micro-Doppler signatures (drones, birds, etc.) may generate target-dependent false alarms, which is contrary to the traditional uniform distribution assumption. In this case, the estimates of traditional CPHD filter will suffer from the abnormal transfer of PHD mass, causing the degradation of filtering performance. This paper studies the individual tracking of group targets with an improved GM-CPHD filter. First, the target-dependent false alarms are modeled with a general independent and identically distributed (I.I.D.) cluster process. Second, the update equations of cardinality and PHD density in target-dependent false alarms are derived. Finally, a practical solution using the Gaussian mixture method is proposed. The effectiveness of the proposed filter is verified by the simulation and experimental results.

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Section: Target-dependent False Alarmsmentioning

confidence: 99%

A Gaussian Mixture CPHD Filter for Multi-Target Tracking in Target-Dependent False Alarms

Jiang,

Wang,

Dou

et al. 2024

Remote Sensing

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“…Although the extraction method of the micro-Doppler signature in an FMCW radar was presented in refs [29,30], the periodicity in the spectrogram is not clearly visible. Other methods for detecting micro-Doppler signals from drones by using radar systems with different dwell times and multistatic radar can be found in refs [31][32][33][34]. All the methods mentioned above require detection of signals reflected from drone propellers.…”

Section: Introductionmentioning

confidence: 99%

A design of a high‐resolution frequency modulated continuous wave radar for drone detection based on spurious phase noise and discrete clutter reduction

Vu Hop,

Cao Quyen,

Van Loi

2023

IET Radar Sonar & Navi

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The authors deal with the problem of the design and manufacture of a high‐resolution FMCW radar for drone detection and classification. The difficulties of the problem are discrete clutter reduction and spurious phase noise mitigation. The discrete clutter is due to the reflected signals from land vehicles, birds etc., while the spurious phase noise is inherent in the radar signal due to the phase‐locked loop component and leakage between the transmitting and receiving paths. Both spurious phase noise and clutter will increase the system noise level and hence reduce the probability of detection of small targets such as drones and induce false alarms on the radar screen. In order to reduce discrete clutter, the authors propose a method to separate a drone from discrete clutter based on the design of the radar system parameters for a drone and its propeller detection, target's Doppler dispersion and moving characteristics. For spur mitigation, a method that focuses on the design of the isolation coefficient between transmitting and receiving paths to decrease the power of spurs below the minimum power requirement at the input of the analogue‐to‐digital converter is introduced. The results were applied by the authors to the development and manufacture of a radar with the given specifications for drone detection and classification. Different laboratory and field tests show that the spurs are mitigated and the drones are separated from discrete clutter with a range and accuracy better than the one recently published.

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“…The combination of the staring array radar and a multistatic radar network can further improve the performance of a drone detection and tracking system. Small scale multistatic radar networks have successfully demonstrated an ability to detect and track drones; in particular, the NetRad and NeXtRAD radar [4] [5]. These are active multistatic radar networks operating in the S, L and X-band.…”

Section: Introductionmentioning

confidence: 99%

Development of a Passive Dual Channel Receiver at L-Band for the Detection of Drones

Griffin

Balleri

Baker

et al. 2022

2021 18th European Radar Conference (EuRAD)

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Staring radars use a transmitting static wide-beam antenna and a directive digital array to form multiple simultaneous beams on receive. Because beams are fixed, the radar can employ long integration times to detect slow low-RCS targets, such as drones, which present a challenge to traditional air surveillance radar. The use of multiple spatially separated receivers cooperating with the staring transmitters in a multistatic network allows multi-perspective target acquisitions that can help mitigate interference and ultimately enhance the detection of drones and reduce estimation errors. Here, the development and experimental results of a passive, dual-channel, L-band receiver are presented. The receiver has been used to take measurements of both moving vehicles of drones in flight using a bistatic staring transmitter. An analysis of the receiver is presented using GPS is used to quantify the estimation performance of the receiver.

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Measurements of Multistatic X&L Band Radar Signatures of UAVS

Cited by 7 publications

References 10 publications

A Gaussian Mixture CPHD Filter for Multi-Target Tracking in Target-Dependent False Alarms

A Gaussian Mixture CPHD Filter for Multi-Target Tracking in Target-Dependent False Alarms

A design of a high‐resolution frequency modulated continuous wave radar for drone detection based on spurious phase noise and discrete clutter reduction

Development of a Passive Dual Channel Receiver at L-Band for the Detection of Drones

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