Deep Learning on Multi-Sensor Data for Counter UAV Applications- A Systematic Review is a paper by Stamatios Samaras, Eleni Diamantidou, Dimitrios Atalogu, Nikos Sakellariou, Anastasios Vafeiadis, Antonios Lalas, Anastasios Dimou, Dimitrios Zarpalas, Konstantinos Votis, Petros Daras, and Dimitrios Tzovaras.

Unmanned Aerial Vehicles (UAVs) are experiencing rapid growth across various consumer applications due to their autonomy and adaptability in various environments and tasks. However, this adaptability also introduces evolving threats from malicious actors who can exploit UAVs for criminal activities, turning them into potential threats. This has led to advancements in counter-UAV (c-UAV) applications to safeguard critical infrastructure and significant events from such threats.

Modern c-UAV applications encompass systems equipped with diverse sensors, including electro-optical, thermal, acoustic, radar, and radio frequency sensors. These sensors provide valuable data that can be fused to enhance the accuracy of threat identification. Real-time surveillance is crucial for promptly detecting adverse events or conditions, but it presents numerous challenges, including object detection, classification, multi-object tracking, and multi-sensor data fusion.

In recent years, researchers have explored the application of deep learning methodologies to address these challenges in the context of generic objects, achieving significant progress. However, applying deep learning to UAV detection and classification represents a relatively new concept. Consequently, there is a need for a comprehensive overview of deep learning technologies applied to c-UAV tasks involving data from various sensors and multi-sensor information fusion. This paper aims to provide insights into the recent advances in deep learning for c-UAV tasks, offering a valuable resource for enhancing c-UAV applications in the future.

Publication Date– 2019

Deep Learning on Multi-Sensor Data for Counter UAV Applications- A Systematic Review contains the following major sections:

  • Introduction
  • Radar Sensor
  • Optical Sensor
  • Thermal Sensor
  • Acoustic Sensor
  • Multi-Sensor Fusion
  • Discussion and Recommendations
  • Conclusions

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Authors- Stamatios Samaras, Eleni Diamantidou, Dimitrios Atalogu, Nikos Sakellariou, Anastasios Vafeiadis, Antonios Lalas, Anastasios Dimou, Dimitrios Zarpalas, Konstantinos Votis, Petros Daras, and Dimitrios Tzovaras

See Also-

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

Drone Detection and Tracking by Fusion of Sensor Modalities

Post Image- Recommended counter-UAV system (Image Credit: Authors)