Detection of MAVs (Micro Aerial Vehicles) based on millimeter wave radar

Noetel, Denis; Johannes, Winfried; Caris, M.; Hommes, Alexander; Stanko, Stephan

doi:10.1117/12.2242020

Cited by 5 publications

(9 citation statements)

References 1 publication

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“…The current state-of-the-art in moving object detection problem, whether it is detecting a big drone or a small bird, involves some form of radar. Evaluating the feasibility of using radar for UAV detec- tion has received considerable attention [2], [6]- [8]. However, there are some practical limitations and cost considerations when designing and deploying a radar suitable for detecting drones.…”

Section: Radarsmentioning

confidence: 99%

“…In terms of wave modulation, 𝐶𝑊 (Continues Wave) radars require significantly less power than pulsed versions and, thus, 𝐶𝑊 radars are more attractive for small UAV detection. Many of the available research papers (e.g., [2]) use 𝐹 𝑀𝐶𝑊 (Frequency Modulated Continues Wave) radars for drone detection due to their lower power consumption compared to other wave modulation approaches.…”

Section: Radar Signal Powermentioning

confidence: 99%

“…Most active radar scenarios where the transmitter and receiver are bundled together are usually either static multichannel or use a mechanical rotation for both reception and transmission of signals. As an example, in [2], Noetel et al investigate two methods of scanning. In the first scenario, they used a scanning surveillance radar system which is a mechanically scanning 𝐹 𝑀𝐶𝑊 system operating in 94 GHz (they used mmWave radar).…”

Section: F Mechanical or Multi-channel Scanningmentioning

confidence: 99%

“…Given the proliferation of drones and their use for both good and bad, there is an urgent need for accurate drone detection and classification into permitted and not-allowed while tracking their trajectory. Currently, there are different methods for detecting drones in the airspace: radars (e.g., [2]), acoustic sensors (e.g., [3]), Radio Frequency (RF) signal detection (e.g., [4]), visual and optical sensors (e.g., [5]), as shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Securing your Airspace: Detection of Drones Trespassing Protected Areas

Famili¹,

Stavrou²,

Wang³

et al. 2021

Preprint

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There has been a rapid growth in the deployment of Unmanned Aerial Vehicles (UAVs) in various applications ranging from vital safety-of-life such as surveillance and reconnaissance at nuclear power plants to entertainment and hobby applications. While popular, drones can pose serious security threats that can be unintentional or intentional. Thus, there is an urgent need for real-time accurate detection and classification of drones. In this article, we perform a survey of drone detection approaches presenting their advantages and limitations. We analyze detection techniques that employ radars, acoustic and optical sensors, and emitted radio frequency (RF) signals. We compare their performance, accuracy, and cost, concluding that combining multiple sensing modalities might be the path forward.

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Section: Radarsmentioning

confidence: 99%

Section: Radar Signal Powermentioning

confidence: 99%

Section: F Mechanical or Multi-channel Scanningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Securing your Airspace: Detection of Drones Trespassing Protected Areas

Famili¹,

Stavrou²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The drone popularity, mixed use cases, and diverse environmental conditions have only exacerbated the detection challenges [36][37][38][39][40]. Currently, there are different methods for detecting drones in the airspace: active radars (e.g., [41]), passive radars (e.g., using spaceborne illuminators of opportunity [42]), acoustic sensors (e.g., [43]), Radio Frequency (RF) signal detection (e.g., [44]), and visual and optical sensors (e.g., [45]), as shown in Figure 1. The drone detection system is typically deployed in close proximity to the area of interest.…”

Section: Introductionmentioning

confidence: 99%

Securing Your Airspace: Detection of Drones Trespassing Protected Areas

Famili,

Stavrou,

Wang

et al. 2024

Sensors

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Unmanned Aerial Vehicle (UAV) deployment has risen rapidly in recent years. They are now used in a wide range of applications, from critical safety-of-life scenarios like nuclear power plant surveillance to entertainment and hobby applications. While the popularity of drones has grown lately, the associated intentional and unintentional security threats require adequate consideration. Thus, there is an urgent need for real-time accurate detection and classification of drones. This article provides an overview of drone detection approaches, highlighting their benefits and limitations. We analyze detection techniques that employ radars, acoustic and optical sensors, and emitted radio frequency (RF) signals. We compare their performance, accuracy, and cost under different operating conditions. We conclude that multi-sensor detection systems offer more compelling results, but further research is required.

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Prediction of microunmanned aerial vehicle flight behavior from two-dimensional intensity images

2019

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The increasing number of microunmanned aerial vehicles (MUAVs) is a rising risk for personal privacy and security of sensitive areas. Owing to the highly agile maneuverability and small cross section of the MUAV, effective countermeasures (CMs) are hard to deploy, especially when a certain temporal delay occurs between the localization and the CM effect. Here, a reliable prediction of the MUAV flight behavior can increase the effectiveness of CMs. We propose a pose estimation approach to derive the three-dimensional (3-D) flight path from a stream of two-dimensional intensity images. The pose estimation in a single image results in an estimation of the current position and orientation of the quadcopter in 3-D space. Combined with flight behavior model, this information is used to reconstruct the flight path and to predict the flight behavior of the MUAV. In our laboratory experiments, we obtained a standard deviation between 1 and 24 cm in a five-frame prediction of the 3-D position, depending in the actual flight behavior.

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Detection of MAVs (Micro Aerial Vehicles) based on millimeter wave radar

Cited by 5 publications

References 1 publication

Securing your Airspace: Detection of Drones Trespassing Protected Areas

Securing your Airspace: Detection of Drones Trespassing Protected Areas

Securing Your Airspace: Detection of Drones Trespassing Protected Areas

Prediction of microunmanned aerial vehicle flight behavior from two-dimensional intensity images

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