There are two primary defects in the existing UAV avoidance systems: the system is memoryless; airborne radars are used to detect long-distance barriers, which are unreliable and expensive. The paper adopts the deep learning algorithm and ADS-B communication system based on a satellite base station to solve the above problems. It divides the avoidance problem into two parts: short-distance obstacle avoidance and long-distance route planning. On the one hand, the system establishes the knowledge base storing the previous avoidance experience and the matching mechanism, realizing the correspondence between input and experience through a deep learning algorithm. They can dramatically improve the reaction speed and safety of UAVs. On the other hand, the system realizes the interconnection between UAV and the satellite base station through the ADS-B communication system to replace the radars, putting the task of route planning on the satellite platform. Therefore, the satellite can achieve large-scale and all-weather detection to improve the overall safety of UAVs depending on its high and long-range characteristics. The paper also illustrates the design elements of the RF baseband integrated ADS-B transceiver and the simulation performance of the short-distance avoidance system in the end, whose results show that the system can be applied to dense obstacle environments and significantly improve the security of UAVs in a complex domain.

中文翻译：

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基于ADS-B的可开发无人机自动避让系统设计

现有的无人机避让系统存在两个主要缺陷：系统无记忆性；机载雷达用于探测远距离障碍物，这些障碍物既不可靠又昂贵。本文采用深度学习算法和基于卫星基站的ADS-B通信系统来解决上述问题。它将避障问题分为两部分：短距离避障和长距离路线规划。一方面，系统建立知识库存储以往的回避经验和匹配机制，通过深度学习算法实现输入与经验的对应。它们可以显着提高无人机的反应速度和安全性。另一方面，该系统通过ADS-B通信系统实现无人机与卫星基站的互联，替代雷达，将航路规划任务置于卫星平台上。因此，卫星可以实现大范围、全天候的探测，以提高无人机的整体安全性，取决于其高远射程的特点。论文最后还阐述了射频基带集成ADS-B收发器的设计要素和短距离避让系统的仿真性能，结果表明该系统可以应用于密集障碍物环境，显着提高了安全性。复杂领域中的无人机。卫星可以实现大范围、全天候的探测，以提高无人机的整体安全性，取决于其高远射程的特点。论文最后还阐述了射频基带集成ADS-B收发器的设计要素和短距离避让系统的仿真性能，结果表明该系统可以应用于密集障碍物环境，显着提高了安全性。复杂领域中的无人机。卫星可以实现大范围、全天候的探测，以提高无人机的整体安全性，取决于其高远射程的特点。论文最后还阐述了射频基带集成ADS-B收发器的设计要素和短距离避让系统的仿真性能，结果表明该系统可以应用于密集障碍物环境，显着提高了安全性。复杂领域中的无人机。