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 system’s tracking capabilities (both in coverage and distance), as well as its accuracy are validated and compared to State Of The Art (SOTA) models, demonstrating improved performance, particularly in terms of coverage and maximum tracking distance. Our approach achieved accuracy on par with the SOTA indoor method while increasing the maximum detection range by approximately 85% beyond the SOTA outdoor method to 130m. Additionally, our method yields at least a twofold increase in track coverage and returned point counts.