Correction: Comparison of RADAR, Passive Optical with Acoustic, and Fused Multi-Modal Active and Passive Sensing for UAS Traffic Management Compliance and Urban Air Mobility Safety

Sam Siewert, M. Andalibi, S. Bruder, Jonathan M. Buchholz, Doug Chamberlain, Alexandra L. Lindsey, Trevis Shiroma, David Stockhouse
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引用次数: 1

Abstract

Embry Riddle Aeronautical University Prescott has designed and prototyped a small UAS (Unmanned Aerial System) detection, tracking, classification and identification system for UTM (UAS Traffic Management) compliance verification and UAM (Urban Air Mobility). This system primarily uses passive optical and acoustic sensors along with GNSS (Global Navigation Satellite Systems), or GPS (Global Positioning System) and ADS-B (Automatic Dependent Surveillance Broadcast). The system design, known as Drone Net, is a network of passive sensors designed to cover a kilometer square area. In this paper we present the results of experiments to add an active RADAR (Radio Detection and Ranging) on the ground and active LIDAR (Light Detection and Ranging) on flight nodes (small UAS). System tests completed previously have shown feasibility for all passive optical and acoustic UTM and UAM. The point of the RADAR testing is to directly compare passive sensor networks to active to determine the value of each alone and to test the hypothesis that multimodal active and passive sensing will be superior, but likely at higher cost than passive alone. Previously, we completed coordinated experiments with an EO/IR camera system and acoustic sensor network with an Allsky hemispherical six-camera system with resolution up to 12 million pixels to optimize detection and tracking. For some applications such as corporate campuses and Class-D airports, passive sensing alone might be sufficient and most cost effective, but for urban, military, and higher traffic Class-C and Class-B larger airports, we believe the combined multi-modal purpose-built sUAS RADAR integrated with optical and acoustic sensor networks will be most effective. Based upon our preliminary results presented herein, purpose built small RADAR with wide fields of view configured into an All-sky active system along with optical All-sky cameras and acoustic sensors are ideal for urban locations requiring the highest confidence in monitoring of combined UTM, UAM and GA (General Aviation) traffic. UTM will mostly be small UAS delivering parcels and must share airspace with UAM carrying passengers for short-hop transportation along with general aviation covering longer distances, but also entering urban airspace at airports. Clearly the airspace in urban locations is going to become much more congested, with new risk, but also new opportunity to improve transportation overall. The results we present here are a start at answering basic questions about multi-modal sensor effectiveness for urban navigation, both passive and active. In scenarios where sUAS and UAM may not have reliable GNSS or might not be UTM compliant, the ground detection, tracking and localization is most critical for assured urban airspace safety and security.
修正:雷达、被动光学与声学、融合多模态主动与被动传感对无人机交通管理合规性和城市空中交通安全的比较
Embry Riddle航空大学Prescott设计了一个小型UAS(无人机系统)检测、跟踪、分类和识别系统的原型,用于UTM(无人机交通管理)合规性验证和UAM(城市空中机动)。该系统主要使用无源光学和声学传感器以及GNSS(全球导航卫星系统)或GPS(全球定位系统)和ADS-B(自动相关监视广播)。该系统设计被称为无人机网络,是一个被动传感器网络,旨在覆盖一平方公里的区域。在本文中,我们介绍了在地面上添加有源雷达(无线电探测和测距)和在飞行节点(小型无人机)上添加有源激光雷达(光探测和测距)的实验结果。之前完成的系统测试显示了所有无源光学和声学UTM和UAM的可行性。RADAR测试的重点是直接比较被动传感器网络和主动传感器网络,以确定每个传感器网络单独的价值,并验证多模态主动和被动传感器网络更优越的假设,但可能比被动传感器网络的成本更高。之前,我们完成了EO/IR相机系统和声学传感器网络的协调实验,Allsky半球六摄像头系统的分辨率高达1200万像素,以优化检测和跟踪。对于一些应用,如企业园区和d类机场,单靠被动传感可能就足够了,而且最具成本效益,但对于城市、军事和更高流量的c类和b类大型机场,我们认为将多模态专用sUAS雷达与光学和声学传感器网络集成在一起将是最有效的。根据我们在本文中提出的初步结果,专用小型雷达具有宽视场,配置为全天主动系统,以及光学全天相机和声学传感器,非常适合城市位置,需要最高的信心来监测UTM, UAM和GA(通用航空)交通。UTM将主要是运送包裹的小型无人机,必须与载有乘客的UAM共享空域,进行短途运输,以及覆盖较长距离的通用航空,但也要进入机场的城市空域。显然,城市地区的空域将变得更加拥挤,这带来了新的风险,但也带来了改善整体交通的新机遇。我们在这里提出的结果是回答关于城市导航多模态传感器有效性的基本问题的开始,包括被动和主动。在sUAS和UAM可能没有可靠的GNSS或可能不符合UTM的情况下,地面探测、跟踪和定位对于确保城市空域安全和保障至关重要。
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