Drone Tracking Based on the Fusion of Staring Radar and Camera Data: An Experimental Study

This paper presents an experimental study on tracking a small drone target with a high resolution camera and a staring radar. The objective is to assess the benefits of fusing the outputs of both sensors using real data collected during live drone trials. We examine the impact of losing the signal from one sensor, which often occurs in practice for various reasons such as occlusions, high background noise-clutter, target sharp maneuvers, etc. We demonstrate that fusion with filtering, namely employing interacting multiple models with unscented Kalman filter in modified spherical coordinates or a simple extended Kalman filter, can deliver improved overall target tracking performance under such degraded sensing conditions.