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Multi-source sensor fusion for small unmanned aircraft systems using fuzzy logic

机译:基于模糊逻辑的小型无人机系统多源传感器融合

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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.
机译:由于使用小型无人机系统(SUA)超越视野(BVLOS)的应用程序继续在未来几年增长,因此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准确地跟踪车辆位置,以确保没有两个车辆彼此碰撞,没有车辆撞到周围结构,并识别偏离名义场景。在这项研究中,智能系统方法用于估计苏斯的位置,包括各种传感器平台,包括GPS,雷达和车载检测硬件。常见的研究挑战包括多种传感器平台和传感器可靠性。努力解决这些挑战,使用诸如最大后验估计和基于模糊的基于逻辑的传感器置信度确定的技术。

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