Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, 28040 Madrid, Spain.
Sensors (Basel). 2021 Dec 28;22(1):189. doi: 10.3390/s22010189.
Noncollaborative surveillance of airborne UAS (Unmanned Aerial System) is a key enabler to the safe integration of UAS within a UTM (Unmanned Traffic Management) ecosystem. Thus, a wide variety of new sensors (known as Counter-UAS sensors) are being developed to provide real-time UAS tracking, ranging from radar, RF analysis and image-based detection to even sound-based sensors. This paper aims to discuss the current state-of-the art technology in this wide variety of sensors (both academically and commercially) and to propose a set of simulation models for them. Thus, the review is focused on identifying the key parameters and processes that allow modeling their performance and operation, which reflect the variety of measurement processes. The resulting simulation models are designed to help evaluate how sensors' performances affect UTM systems, and specifically the implications in their tracking and tactical services (i.e., tactical conflicts with uncontrolled drones). The simulation models cover probabilistic detection (i.e., false alarms and probability of detection) and measurement errors, considering equipment installation (i.e., monostatic vs. multistatic configurations, passive sensing, etc.). The models were integrated in a UTM simulation platform and simulation results are included in the paper for active radars, passive radars, and acoustic sensors.
非协作式的 UAS（无人机系统）空中监测是非配合式 UAS 在 UTM（无人机交通管理）生态系统中安全集成的关键推动因素。因此，各种各样的新型传感器（称为反无人机传感器）正在被开发出来，以提供实时的 UAS 跟踪，包括雷达、射频分析和基于图像的检测，甚至基于声音的传感器。本文旨在讨论这些广泛的传感器（学术和商业）的当前最先进技术，并为它们提出一组模拟模型。因此，本综述的重点是确定允许对其性能和操作进行建模的关键参数和过程，这些参数和过程反映了各种测量过程。所得到的模拟模型旨在帮助评估传感器的性能如何影响 UTM 系统，特别是在其跟踪和战术服务方面的影响（即与不受控无人机的战术冲突）。模拟模型涵盖了概率检测（即误报和检测概率）和测量误差，同时考虑了设备安装（即单基地与多基地配置、无源感应等）。这些模型已集成到 UTM 模拟平台中，并在本文中包含了有源雷达、无源雷达和声学传感器的模拟结果。
