Multi-Sensory Data Fusion in Terms of UAV Detection in 3D Space

Dudczyk, J.; Czyba, Roman; Skrzypczyk, Krzysztof

doi:10.3390/s22124323

Cited by 12 publications

(3 citation statements)

References 33 publications

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“…All of the sensor modalities have limitations that can render them unreliable under certain environmental and weather conditions [134,135]. We posit that a robust drone detection system should rely on more than one sensing modality.…”

Section: Multi-sensor Approachmentioning

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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Section: Multi-sensor Approachmentioning

confidence: 99%

Securing Your Airspace: Detection of Drones Trespassing Protected Areas

Famili,

Stavrou,

Wang

et al. 2024

Sensors

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“…The problem of intrapulse modulation recognition with a fusion network is presented in [ 4 ]. Data fusion techniques are successfully used in areas such as tracking systems [ 11 ] or multisensory systems for unnamed aerial vehicles (UAV) detection [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

An Application of Analytic Wavelet Transform and Convolutional Neural Network for Radar Intrapulse Modulation Recognition

Walenczykowska

Kawalec

Krenc

2023

Sensors

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This article analyses the possibility of using the Analytic Wavelet Transform (AWT) and the Convolutional Neural Network (CNN) for the purpose of recognizing the intrapulse modulation of radar signals. Firstly, the possibilities of using AWT by the algorithms of automatic signal recognition are discussed. Then, the research focuses on the influence of the parameters of the generalized Morse wavelet on the classification accuracy. The paper’s novelty is also related to the use of the generalized Morse wavelet (GMW) as a superfamily of analytical wavelets with a Convolutional Neural Network (CNN) as classifier applied for intrapulse recognition purposes. GWT is used to obtain time–frequency images (TFI), and SqueezeNet was chosen as the CNN classifier. The article takes into account selected types of intrapulse modulation, namely linear frequency modulation (LFM) and the following types of phase-coded waveform (PCW): Frank, Barker, P1, P2, and Px. The authors also consider the possibility of using other time–frequency transformations such as Short-Time Fourier Transform(STFT) or Wigner–Ville Distribution (WVD). Finally, authors present the results of the simulation tests carried out in the Matlab environment, taking into account the signal-to-noise ratio (SNR) in the range from −6 to 0 dB.

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“…The researchers utilise electromagnetic [4], acoustic [5], video [6], and radar [7] data for algorithm development. Each of these techniques has drawbacks, and as a result, several systems with heterogeneous sensors and data fusion methods are demonstrated to satisfy practical requirements [8].…”

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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Multi-Sensory Data Fusion in Terms of UAV Detection in 3D Space

Cited by 12 publications

References 33 publications

Securing Your Airspace: Detection of Drones Trespassing Protected Areas

Securing Your Airspace: Detection of Drones Trespassing Protected Areas

An Application of Analytic Wavelet Transform and Convolutional Neural Network for Radar Intrapulse Modulation Recognition

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

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