A Gaussian Process model for UAV localization using millimetre wave radar

• Published in: Expert systems with applications Vol. 185; p. 115563
• Main Authors: Paredes, José A., Álvarez, Fernando J., Hansard, Miles, Rajab, Khalid Z.
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.12.2021; Elsevier BV
• Subjects: Algorithms; Doppler effect; Drone; Drones; Estimates; Gaussian process; Localization; Millimeter waves; Millimetre wave; Positioning system; Propellers; Radar; Radar data; Radar detection; Regression models; Spectrum analysis; Unmanned aerial vehicles; Visibility; Positioning system; Millimetre wave; Drone; Radar
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2021.115563

The detection and positioning of unmanned aerial vehicles has become essential for both automation and surveillance tasks, in recent years. The design of accurate drone localization systems is challenging, especially in cluttered environments, where the target may be partially or even completely obscured. This paper proposes a precise detection and 3D localization system for drones, by means of a millimetre wave radar. Drone locations are estimated from spatial heatmaps of the received radar signals, which are obtained by applying the super-resolution MUSIC algorithm. These estimates are improved by analysis of the micro-Doppler effect, generated by the rotating propellers, which aids detection in poor visibility conditions. A novel Gaussian Process Regression model is developed, in order to compensate for systematic biases in the radar data. The complete system produces accurate estimates of the target range and direction, and is shown to outperform direct spectral analysis methods. •A new UAV localization system is presented, based on millimetre wave radar data.•The micro-Doppler effect is used to improve target detection.•A Gaussian Process regression model is developed, in order to overcome noise and bias in the radar data.•The system is shown to perform well in cluttered environments.