RF Drone Detection System Based on a Distributed Sensor Grid With Remote Hardware-Accelerated Signal Processing is a work by Przemyslaw Flak and Roman Czyba.

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have transitioned from military origins to a wide range of civilian applications, introducing new possibilities in various everyday services. The surge in consumer interest in amateur drones equipped with advanced cameras poses significant risks to public safety and privacy. In response, the literature presents various sensing techniques for drone detection, utilizing different physical phenomena. Among these, passive radiofrequency sensing stands out as it can detect a drone before takeoff and locate the operator.

A spectrogram-based method has been developed and optimized for computing location, enabling the deployment of sensor grids over standard Ethernet networks. Hardware-accelerated energy sensing extracts data frames from background noise during the detection phase. Machine learning identifies drone presence, focusing on preamble pattern recognition to minimize computational effort. This method has been evaluated using an open-source dataset in an isolated setting and fine-tuned across multiple neural network architectures.

Subsequently, the complete sensor processing chain was tested in a real-life scenario. The analytical energy detector stage achieved an approximate signal-to-noise ratio (SNR) margin of -8.7 dB. With 1.1 million parameters, the proposed neural network reached 99.93% accuracy in simulations within an SNR range down to -9.5 dB. Even after quantization for embedded platform implementation, the device can function as a stand-alone early intrusion detector or as part of a distributed sensor grid.

Publication Date– December 2023

RF Drone Detection System Based on a Distributed Sensor Grid With Remote Hardware-Accelerated Signal Processing contains the following major sections:

  • Introduction
  • System Model
  • Related Wor
  • Proposed Solution
  • Experimental Results and Discussion

Open Access Paper. This article is distributed under the terms of the Attribution-Non-Commercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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