  A unique approach for the mid-air autonomous aerial interception of
non-cooperating UAV by a flying robot equipped with a net is presented in this
paper. A novel interception guidance method dubbed EPN is proposed, designed to
catch agile maneuvering targets while relying on onboard state estimation and
tracking. The proposed method is compared with state-of-the-art approaches in
simulations using 100 different trajectories of the target with varying
complexity comprising almost 14 hours of flight data, and EPN demonstrates the
shortest response time and the highest number of interceptions, which are key
parameters of agile interception. To enable robust transfer from theory and
simulation to a real-world implementation, we aim to avoid overfitting to
specific assumptions about the target, and to tackle interception of a target
following an unknown general trajectory. Furthermore, we identify several often
overlooked problems related to tracking and estimation of the target's state
that can have a significant influence on the overall performance of the system.
We propose the use of a novel state estimation filter based on the IMM filter
and a new measurement model. Simulated experiments show that the proposed
solution provides significant improvements in estimation accuracy over the
commonly employed KF approaches when considering general trajectories. Based on
these results, we employ the proposed filtering and guidance methods to
implement a complete autonomous interception system, which is thoroughly
evaluated in realistic simulations and tested in real-world experiments with a
maneuvering target going far beyond the performance of any state-of-the-art
solution.
