基于模糊逻辑的小型无人机多源传感器融合

Brandon Cook, Kelly Cohen
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引用次数: 4

摘要

随着小型无人机系统(sUAS)超视距(BVLOS)的应用在未来几年继续增长,探索智能传感器融合技术势在必行。在BVLOS场景中,车辆位置必须随着时间的推移而精确跟踪,以确保没有两辆车相互碰撞,没有车辆撞到周围的建筑物,并识别非标称场景。在本研究中,使用智能系统方法来估计sUAS的位置,给出各种传感器平台,包括GPS,雷达和机载探测硬件。常见的研究挑战包括多传感器平台和传感器可靠性。为了解决这些挑战,使用了诸如最大后验估计和基于模糊逻辑的传感器置信度确定等技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-source sensor fusion for small unmanned aircraft systems using fuzzy logic
As the applications for using small Unmanned Aircraft Systems (sUAS) beyond visual line of sight (BVLOS) continue to grow in the coming years, it is imperative that intelligent sensor fusion techniques be explored. In BVLOS scenarios the vehicle position must accurately be tracked over time to ensure no two vehicles collide with one another, no vehicle crashes into surrounding structures, and to identify off-nominal scenarios. In this study, an intelligent systems approach is used to estimate the position of sUAS given a variety of sensor platforms, including GPS, radar, and onboard detection hardware. Common research challenges include multiple sensor platforms and sensor reliability. In an effort to resolve these challenges, techniques such as a Maximum a Posteriori estimation and Fuzzy Logic based sensor confidence determination are used.
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