UAV Detection Multi-sensor Data Fusion

Abstract

In today's world, the ubiquitous presence of unmanned aerial vehicles (UAVs) poses unprecedented challenges, ranging from privacy concerns and security threats to potential safety hazards. Strong and precise drone detection techniques are essential as drones are incorporated into a wider range of sectors. Traditional single-sensor approaches encounter limitations, such as susceptibility to environmental conditions and restricted detection accuracy. This paper addresses the significance of drone detection in our modern context, highlighting the critical need for comprehensive and efficient solutions. The challenges associated with depending solely on a single sensor for drone detection are explored, emphasizing issues like limited adaptability to environmental variations and the potential for false positives or negatives. Subsequently, the paper delves into the advantages of employing sensor fusion, specifically integrating radar and camera information using the Kalman Filter. This approach enhances accuracy and efficiency by leveraging the complementary strengths of radar and camera sensors. The Kalman Filter provides a dynamic framework to model the linear nature of drone movements, enabling precise localization. The fusion of radar and camera data not only addresses the limitations of single-sensor systems but also ensures adaptability to diverse operational scenarios, making it a promising solution for reliable and real-time drone detection in our dynamic and evolving world.