UAV Micro-Doppler Signature Analysis Using DVB-S Based Passive Radar

Ummenhofer, Martin; Lavau, Louis Cesbron; Cristallini, Diego; O’Hagan, Daniel

doi:10.1109/radar42522.2020.9114747

Cited by 24 publications

(7 citation statements)

References 17 publications

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“…The passive DVB-S and FSR were also used in [233] for the detection of multi-rotor UAVs and performing micro-Doppler analysis of the copter UAVs. In [234], DVB-S was used for the detection of multi-rotor UAVs and micro-Doppler signature extraction. The micro-Doppler signature helped in the UAV classification.…”

Section: Gmentioning

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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Section: Gmentioning

confidence: 99%

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

Khawaja¹

2022

Preprint

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“…It is also possible to use the micro-Doppler shifts in radar echoes generated by rotating blades to distinguish between the drone object and the bird [13]. Although the passive radar concept is feasible [14], ever smaller drone sizes, low altitude operation, low flying speed, and the urban environment highly reduce this detector's accuracy.…”

Section: Introductionmentioning

confidence: 99%

RF Drone Detection System Based on a Distributed Sensor Grid With Remote Hardware-Accelerated Signal Processing

Flak,

Czyba

2023

IEEE Access

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Unmanned Aerial Vehicles (UAVs), sometimes known as drones, evolved from military to civilian applications, opening up novel perspectives in a variety of everyday services. The rapidly growing consumer interest in amateur drones equipped with high-end cameras compromises the everyday safety and privacy of people. In the literature, a variety of sensing techniques based on different physical phenomena have been proposed for drone detection. Among acoustic, optical, or radar detection systems, passive radiofrequency sensing is the only one that can identify a drone even before it takes off and additionally indicate the operator's location. A spectrogram-based method is developed and optimised in terms of computing location, resulting in the possibility of sensor grid deployment over a standard Ethernet network. The detection phase involves hardware-accelerated energy sensing to extract the data frames from the background noise. Drone presence is then identified using machine learning based solely on preamble pattern recognition, which reduces the computational effort. The presented procedure is evaluated in an isolated setting employing an open-source dataset and tuned across multiple neural network architectures. Next, the complete sensor processing chain is examined in a real-life scenario. The analytical energy detector stage reaches a margin of roughly -8.7 dB in the signal-to-noise (SNR) ratio. With 1.1 M parameters, the proposed neural network achieves 99.93% simulation accuracy in up to -9.5 dB SNR range. Even after quantization for embedded platform implementation, the device can be used as a stand-alone early intrusion detector or as part of a distributed sensor grid.INDEX TERMS Convolutional neural network, drones, field programmable gate array, software defined radio, spectrogram, surveillance, unmanned aerial vehicles This article has been accepted for publication in IEEE Access.

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“…Geostationary orbits produce a negligible relative movement whereas medium and low Earth orbits produce variations in PR geometry which affect the acquired signal characteristics. Coherent processing of the reference and surveillance signals can be applied when the platform movement is negligible and the transmission and reception characteristics allow the direct isolation of the desired illumination signal such as in DVB-S based PRs [7,8]. More complex processing schemes should be applied in other cases, such as Global Navigation Satellite System (GNSS) based PRs, where the platform movement and the medium access technique require the isolation and tracking of the reference signal.…”

Section: Introductionmentioning

confidence: 99%

Improved 2D Ground Target Tracking in GPS-Based Passive Radar Scenarios

Gómez-del-Hoyo

del-Rey-Maestre

Jarabo-Amores

et al. 2022

Sensors

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Global Positioning System (GPS) satellites offer promising opportunity for Passive Radar systems due to their global coverage and the availability of multiple satellites throughout the world. However, their low power at ground level limits system coverage. In this paper, a GPS based Passive Radar which exploits a single illumination source, and uses digital array processing for ground targets localization is presented. To face signal power problems, a processing scheme combining reconstructed reference signals, adaptive filtering techniques and spatial filtering is implemented. Conventional beamforming techniques are used to increase the level of the target echo before the detection stage, and high resolution DoA estimation techniques are applied to estimate targets azimuth. Ground target localization in local Cartesian space is performed taking into account the system geometry, range and azimuth information. Both synthetic and real radar data are used to analyse system operation. During the measurement campaign, a cooperative vehicle was used for validation purposes. Results confirm that ground targets detection and localization are feasible using a single GPS transmitter.

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UAV Micro-Doppler Signature Analysis Using DVB-S Based Passive Radar

Cited by 24 publications

References 17 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

RF Drone Detection System Based on a Distributed Sensor Grid With Remote Hardware-Accelerated Signal Processing

Improved 2D Ground Target Tracking in GPS-Based Passive Radar Scenarios

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