MACHINE LEARNING FOR BIO-INSPIRED NAVIGATION

该项目将致力于评估和复制动物在迁徙过程中用于长距离航行的不同方法。最初的重点将是鸟类的感官能力，但该项目将审查所有当前关于动物导航和迁徙的理论。动物有一系列我们人类所缺乏的感觉输入。例如磁性传感器、地标识别和检测偏振光的能力。英国量子技术计划对磁传感技术的改进，使人们对使用磁传感器进行导航的兴趣与日俱增，但电子和其他金属部件造成的杂散磁场效应仍存在一些问题。以前也研究过基于图像的导航，但由于具有区分偏振光的能力，因此有机会将地面特征数据库与来自偏振太阳照明的信息结合起来。PHD的目标将是识别可用于定位、导航和定时(PNT)应用的仿生传感器，并开发一种处理体系结构，使此类传感器成为融合PNT解决方案的一部分。

This project will be concerned with assessing and replicating the different methods that animals use to navigate over long distances during migration. The initial emphasis will be on the sensory ability of birds, but the project will review all current theories for animal navigation and migration. Animals have a range of sensory inputs that we as humans lack. Examples include magnetic sensors, landmark recognition, and the ability to detect polarised light. Improvements to magnetic sensing from the UK quantum technology programme are increasing interest in the use of magnetic sensors for navigation, but there are still issues around the effect of stray magnetic fields caused by electronics and other metal components. Image based navigation has also been studied previously, but with the ability to distinguish polarised light, there is scope to combine ground feature databases with information from polarised solar illumination. The objective of the PhD will be to identify bio-inspired sensors that could be used in Positioning Navigation and Timing (PNT) applications and to develop a processing architecture that would allow such sensors to form part of a fused PNT solution.