Abstract
This paper presents a passive radar system using a signal of opportunity from Digital Video Broadcasting Satellite (DVB-S). The ultimate purpose of the system is to be used as an air traffic monitoring and surveillance system. However, the work focuses on drone detection as a proof of the concept. Detecting a drone by using satellite-based passive radar possess inherent challenges, such as the small radar cross section and low speed. Therefore, this paper proposes a unique method by leveraging the advantage of forward-scattering radar (FSR) topology and characteristics to detect a drone; in other words, the system is known as a passive FSR (p-FSR) system. In the signal-processing algorithm, the empirical mode decomposition (EMD) is applied to the received signal to extract the unique feature vector of the micro-Doppler frequency from the drone’s rotating blades. The paper highlights the p-FSR experimental setup and experiment campaign to detect drones. The experimental results show the feasibility of the p-FSR using a signal transmitted from a satellite to detect flying drone crossing the forward-scatter baseline between the satellite and ground station.
Highlights
A quadcopter drone has provided a significant impact on many applications, including aerial imaging, monitoring, search and rescue, security surveillance, entrepreneurial hobbies, and precision agriculture
In an attempt to overcome these disadvantages, this paper proposes a passive forward-scattering radar (FSR) by utilizing a signal of opportunity from Digital Video Broadcasting Satellite (DVB-S) to detect a drone, which was briefly introduced in the conference paper [9]
This paper provides a comprehensive study on digital video broadcasting satellite (DVB-S) based passive FSR (p-FSR), and a numerical analysis on drone blade’s radar cross section, full-scale experiment and result analysis, as well as new signal processing technique to detect a drone, was presented in the previous paper [10]
Summary
A quadcopter drone has provided a significant impact on many applications, including aerial imaging, monitoring, search and rescue, security surveillance, entrepreneurial hobbies, and precision agriculture. Background noise and sound from other sources like vehicles, birds, and others create some difficulty. An attempt to detect a drone based on video imaging involved the object’s appearance and motion cues, as published in [5]. An object-centric motion was developed to compensate for the changes in the object and the background appearance. This approach can potentially be used for collision avoidance in addition to detection and can, improve a vision-guided tracking algorithm. The system based on image and optical will face difficulty during weather conditions such as rain and snow
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