AI-based framework for drone intent classification using a non-cooperative radar system
使用非合作雷达系统进行无人机意图分类的基于人工智能的框架
基本信息
- 批准号:2744569
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:英国
- 项目类别:Studentship
- 财政年份:2022
- 资助国家:英国
- 起止时间:2022 至 无数据
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
With the proliferation of Unmanned Autonomous Vehicles (UAV) applications (e.g. surveying, medical deliveries etc), systems and services to allow them to co-exist with manned aviation and to be used within controlled airspace are being developed. To ensure safety, it is paramount that aviation users and operators are alerted of potential conflicts between aircraft and between aircraft and UAVs as well. In Europe, so-called 'U3 advanced services' are being developed that include capacity management and systems that support conflict detection and resolution specifically to enable UAV operations. For the manned aviation case, a well-established concept known as Short Term Conflict Alert (STCA) is currently being used as a safety net to alert ATC operators of any situation where user defined minimum separation distances between any pair of cooperative surveillance (radar) tracks are predicted to be violated within a short look ahead time (usually 2 minutes). This is achieved via a visual alert on the radar display, though some systems also provide an audible alert. The system typically uses tracks based on cooperative radar, i.e. a system that acquires position information directly from a transponding aircraft. However, this solution cannot be applied to non-transponding air vehicles, and thus the future blended airspace of manned and unmanned air vehicles will need a more resilient and robust solution, to fully unlock the potential of UAV applications.This PhD proposes to develop a robust conflict detection and alerting mechanism that is based on non-cooperative radar signatures to classify the aircraft intent, as an automated way to improve safety in a blended airspace, whilst meeting the false alerts rate requirements of manned aviation. The use of Metaheuristics and Deep Learning (DL) techniques in the classification of intent and subsequent detection of potential conflicts and their resolution will be investigated, in order to improve the conventional classification and conflict detection algorithms used. The solution will significantly enhance the capabilities of existing non-cooperative radar systems in manned aviation, as well as counter UAV radar systems, enhancing the safety and security of the blended airspace.
随着无人驾驶自动飞行器(UAV)应用(例如测量、医疗递送等)的激增,正在开发允许它们与载人航空共存并在管制空域内使用的系统和服务。为了确保安全,航空用户和运营商必须警惕飞机之间以及飞机与无人机之间的潜在冲突。在欧洲,正在开发所谓的“U3高级服务”,包括容量管理和支持冲突检测和解决的系统,特别是使无人机操作。对于载人飞机的情况,一个被称为短期冲突警报(STCA)的成熟概念目前被用作安全网,以警告ATC操作员任何情况下,用户定义的任何一对合作监视(雷达)航迹之间的最小间隔距离预计将在短的前瞻时间内被违反(通常为2分钟)。这是通过雷达显示器上的视觉警报来实现的,尽管有些系统也提供声音警报。该系统通常使用基于协作雷达的跟踪,即直接从转发飞行器获取位置信息的系统。然而,这种解决方案不能应用于非转发飞行器,因此未来有人和无人飞行器的混合空域将需要一个更有弹性和鲁棒的解决方案,以充分释放无人机应用的潜力。本博士提出开发一种鲁棒的冲突检测和警报机制,该机制基于非合作雷达特征来分类飞行器意图,作为一种自动化的方式来提高混合空域的安全性,同时满足载人航空的错误警报率要求。将研究元分析和深度学习(DL)技术在意图分类和后续潜在冲突检测及其解决方案中的使用,以改进所使用的传统分类和冲突检测算法。该解决方案将显著增强现有非合作雷达系统在载人航空中的能力,以及对抗无人机雷达系统的能力,从而增强混合空域的安全性。
项目成果
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其他文献
吉治仁志 他: "トランスジェニックマウスによるTIMP-1の線維化促進機序"最新医学. 55. 1781-1787 (2000)
Hitoshi Yoshiji 等:“转基因小鼠中 TIMP-1 的促纤维化机制”现代医学 55. 1781-1787 (2000)。
- DOI:
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LiDAR Implementations for Autonomous Vehicle Applications
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:0
- 作者:
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吉治仁志 他: "イラスト医学&サイエンスシリーズ血管の分子医学"羊土社(渋谷正史編). 125 (2000)
Hitoshi Yoshiji 等人:“血管医学与科学系列分子医学图解”Yodosha(涉谷正志编辑)125(2000)。
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Effect of manidipine hydrochloride,a calcium antagonist,on isoproterenol-induced left ventricular hypertrophy: "Yoshiyama,M.,Takeuchi,K.,Kim,S.,Hanatani,A.,Omura,T.,Toda,I.,Akioka,K.,Teragaki,M.,Iwao,H.and Yoshikawa,J." Jpn Circ J. 62(1). 47-52 (1998)
钙拮抗剂盐酸马尼地平对异丙肾上腺素引起的左心室肥厚的影响:“Yoshiyama,M.,Takeuchi,K.,Kim,S.,Hanatani,A.,Omura,T.,Toda,I.,Akioka,
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