  This paper presents our approach to intercepting a faster intruder UAV,
inspired by the MBZIRC 2020 Challenge 1. By utilizing a priori knowledge of the
shape of the intruder's trajectory, we can calculate an interception point.
Target tracking is based on image processing by a YOLOv3 Tiny convolutional
neural network, combined with depth calculation using a gimbal-mounted ZED Mini
stereo camera. We use RGB and depth data from the camera, devising a
noise-reducing histogram-filter to extract the target's 3D position. Obtained
3D measurements of target's position are used to calculate the position,
orientation, and size of a figure-eight shaped trajectory, which we approximate
using a Bernoulli lemniscate. Once the approximation is deemed sufficiently
precise, as measured by the distance between observations and estimate, we
calculate an interception point to position the interceptor UAV directly on the
intruder's path. Our method, which we have significantly improved based on the
experience gathered during the MBZIRC competition, has been validated in
simulation and through field experiments. Our results confirm that we have
developed an efficient, visual-perception module that can extract information
describing the intruder UAV's motion with precision sufficient to support
interception planning. In a majority of our simulated encounters, we can track
and intercept a target that moves 30% faster than the interceptor.
Corresponding tests in an unstructured environment yielded 9 out of 12
successful results.
