基于多参数量化和蒙特卡罗仿真的无人机感知与规避系统风险评估方法

IF 0.1 4区 工程技术 Q4 ENGINEERING, AEROSPACE Aerospace America Pub Date : 2023-09-01 DOI:10.3390/aerospace10090781
Bona P. Fitrikananda, Y. I. Jenie, R. A. Sasongko, Hari Muhammad
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引用次数: 1

摘要

无人机(UAV)使用的增加带来了令人兴奋的可能性,但也带来了风险,特别是在航空领域,无人机危险地靠近商用飞机飞行的实例。发生事故的可能性凸显了采取有效措施降低空中碰撞风险的迫切需要。本研究旨在通过提供一个评级系统来量化SAA系统的参数和操作风险,从而评估SAA系统在操作过程中的有效性,最终使主管部门、开发商和运营商能够做出明智的决策,以达到一定的安全水平。在本研究中量化了七个参数:SAA的探测距离、视野、传感器精度、测量率、系统集成度、入侵者的距离和接近速度。虽然先前的研究分别解决了这些参数的量化,但本研究的主要贡献是将它们整合在一个简单的五级风险评级系统中的综合方法。这种量化是由一个风险评估模拟器补充的,该模拟器能够在蒙特卡洛模拟设置的任意飞行交通的大样本中测试无人机的风险等级,最终得出其最大风险等级。仿真结果表明,SAA系统的安全性得到了提高,其综合最大风险评级值表明了这一点。在这些因素中,SAA系统的探测距离和传感器精度是这一改进的主要驱动因素。这一结论甚至在管制更严格、有五条或三条强制性航线的空中交通中也是一致的。有趣的是,将入侵者的数量增加到50并不会改变结果,因为入侵者被检测到的概率几乎保持不变。另一方面,提高SAA雷达能力对风险评级的影响比执行法规或限制入侵者的影响更显著。
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Risk Assessment Method for UAV’s Sense and Avoid System Based on Multi-Parameter Quantification and Monte Carlo Simulation
The rise in Unmanned Aerial Vehicle (UAV) usage has opened exciting possibilities but has also introduced risks, particularly in aviation, with instances of UAVs flying dangerously close to commercial airplanes. The potential for accidents underscores the urgent need for effective measures to mitigate mid-air collision risks. This research aims to assess the effectiveness of the Sense and Avoid (SAA) system during operation by providing a rating system to quantify its parameters and operational risk, ultimately enabling authorities, developers, and operators to make informed decisions to reach a certain level of safety. Seven parameters are quantified in this research: the SAA’s detection range, field of view, sensor accuracy, measurement rate, system integration, and the intruder’s range and closing speed. While prior studies have addressed these parameter quantifications separately, this research’s main contribution is the comprehensive method that integrates them all within a simple five-level risk rating system. This quantification is complemented by a risk assessment simulator capable of testing a UAV’s risk rating within a large sample of arbitrary flight traffic in a Monte Carlo simulation setup, which ultimately derives its maximum risk rating. The simulation results demonstrated safety improvements using the SAA system, shown by the combined maximum risk rating value. Among the contributing factors, the detection range and sensor accuracy of the SAA system stand out as the primary drivers of this improvement. This conclusion is consistent even in more regulated air traffic imposed with five or three mandatory routes. Interestingly, increasing the number of intruders to 50 does not alter the results, as the intruders’ probability of being detected remains almost the same. On the other hand, improving SAA radar capability has a more significant effect on risk rating than enforcing regulations or limiting intruders.
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来源期刊
Aerospace America
Aerospace America 工程技术-工程:宇航
自引率
0.00%
发文量
9
审稿时长
4-8 weeks
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