一种基于传播模型赋能的传感器盲校准解决方案
A Propagation-model Empowered Solution for Blind-Calibration of Sensors
AI总结：
本文针对廉价或难以接近的传感器校准难题，将传感过程建模为事件传播与传感器测量两个过程，提出半盲和完全盲校准方法，并通过仿真验证其可行性。
摘要：
传感器校准是一项重大挑战，尤其是对于廉价传感器和安装在难以到达位置的传感器而言。无需标准传感器即可对传感器进行校准的可行性被称为盲校准。公开文献中关于完全盲校准的研究非常少。在这项工作中，我们将传感过程建模为两个过程的组合，即事件通过环境传播到传感器的过程以及传感器中的测量过程。基于此，我们提出了一种独特的方法，以两种形式进行校准，即半盲校准和完全盲校准。我们展示了基于仿真的有限结果，显示出令人鼓舞的成果。
英文摘要：
Calibration of sensors is a major challenge especially in inexpensive sensors and sensors installed in inaccessible locations. The feasibility of calibrating sensors without the need for a standard sensor is called blind calibration. There is very little work in the open literature on totally blind calibration. In this work we model the sensing process as a combination of two processes, viz. propagation of the event through the environment to the sensor and measurement process in the sensor. Based on this, we propose a unique method for calibration in two flavours, viz semi-blind and completely-blind calibration. We show limited results based on simulation showing encouraging results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
