Multi-source sensor fusion for small unmanned aircraft systems using fuzzy logic
Multi-source sensor fusion for small unmanned aircraft systems using fuzzy logic
复制标题
使用模糊逻辑的小型无人机系统的多源传感器融合
DOI：
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复制
发表时间：
2017
期刊：
影响因子：
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通讯作者：
Kelly Cohen
中科院分区：
文献类型：
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作者：
Brandon Cook;Kelly Cohen
随着超视距（BVLOS）小型无人机系统（sUAS）的应用在未来几年持续增长，探索智能传感器融合技术势在必行。在超视距场景中，必须随着时间的推移准确跟踪车辆位置，以确保不会有两辆车相互碰撞，不会有车辆撞到周围的结构，并识别非正常场景。在这项研究中，使用智能系统方法来估计小型无人机系统的位置，包括 GPS、雷达和机载检测硬件等各种传感器平台。常见的研究挑战包括多个传感器平台和传感器可靠性。为了解决这些挑战，使用了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。
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.