Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDAR

TL;DR

This paper presents a drone detection and tracking system using a rosette scanning pattern LiDAR, significantly increasing outdoor detection range while maintaining high accuracy through a particle filter and Pan-Tilt platform.

Contribution

It introduces a novel detection and tracking method leveraging non-repetitive rosette pattern LiDAR and a particle filter with re-detection capabilities, extending outdoor detection range.

Findings

Achieved detection accuracy comparable to state-of-the-art indoor methods.

Increased maximum outdoor detection distance by approximately 80%.

Validated system performance through indoor and outdoor experiments.

Abstract

The use of commercial Micro Aerial Vehicles (MAVs) has surged in the past decade, offering societal benefits but also raising risks such as airspace violations and privacy concerns. Due to the increased security risks, the development of autonomous drone detection and tracking systems has become a priority. In this study, we tackle this challenge, by using non-repetitive rosette scanning pattern LiDARs, particularly focusing on increasing the detection distance by leveraging the characteristics of the sensor. The presented method utilizes a particle filter with a velocity component for the detection and tracking of the drone, which offers added re-detection capability. A Pan-Tilt platform is utilized to take advantage of the specific characteristics of the rosette scanning pattern LiDAR by keeping the tracked object in the center where the measurement is most dense. The detection capabilities and accuracy of the system are validated through indoor experiments, while the maximum detection distance is shown in our outdoor experiments. Our approach achieved accuracy on par with the state-of-the-art indoor method while increasing the maximum detection range by approximately 80\% beyond the state-of-the-art outdoor method.