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.

翻译：本文提出了一种独特的空中自主拦截非合作无人机的方法，该方法使用配备捕网的飞行机器人。我们提出了一种新颖的拦截制导方法，称为EPN，旨在捕获敏捷机动的目标，同时依赖机载状态估计与追踪。在仿真中，使用包含近14小时飞行数据的100条不同复杂度的目标轨迹，将所提方法与最先进的方法进行了比较，EPN展示了最短的响应时间和最高的拦截次数，这是敏捷拦截的关键参数。为了实现从理论和仿真到实际应用的稳健迁移，我们旨在避免对目标特定假设的过拟合，并解决对遵循未知通用轨迹目标的拦截问题。此外，我们指出了几个常被忽视的、与目标状态追踪和估计相关的问题，这些问题可能对系统整体性能产生重大影响。我们提出使用一种基于IMM滤波器的新型状态估计滤波器和一个新的测量模型。仿真实验表明，在考虑通用轨迹时，所提方案在估计精度上相比常用的KF方法有显著提升。基于这些结果，我们采用所提出的滤波与制导方法实现了一个完整的自主拦截系统，并在逼真的仿真中进行了全面评估，并在真实世界实验中针对机动目标进行了测试，其性能远超任何最先进的解决方案。