A Bayesian track management scheme for improved multi‐target tracking and classification in drone surveillance radar

Abstract

In this article, a simple, yet effective, Bayesian scheme for tracks maintenance, promotion, and deletion in drone surveillance radar is presented. It enables the simultaneous tracking of the target body and micro‐Doppler components that originate from the motion of rotors (if any) onboard an unmanned air system. This not only delivers more accurate multi‐target tracking, but also substantially improves the radar automatic target classification capability (e.g. discriminating between drone and non‐drone targets). Challenging and diverse real staring radar datasets are used here to demonstrate the efficacy and benefits of the proposed track management approach.

Guest Editorial: Selected papers from RADAR 2022—International Conference on Radar Systems (Edinburgh, UK)

“…A simple and effective Bayesian scheme to maintain tracks in drone surveillance radar is presented in Ref. [10]. The proposed technique allows the simultaneous tracking of the drone body and of micro-Doppler components induced by the motion of rotors onboard an unmanned air system.…”

Multi-Scale Fusion Uncrewed Aerial Vehicle Detection Based on RT-DETR

“…Traditional UAV detection methods mainly include methods based on radar, acoustic waves, electromagnetic, optical, infrared, and other sensors, but they all have some shortcomings. For example, the disadvantage of radar is that it is easily affected by electromagnetic interference or reflection, and the disadvantage of an acoustic wave is that it has a small detection range and is easily affected by environmental noise or wind speed [7]. The disadvantage of electromagnetic interference or reflection is that its detection range is limited by signal strength and frequency, and it is vulnerable to encryption or spoofing [8].…”