Sun Jianguo, Wang Yueyao, Cheng Yinbao, Zhu Guanghu, Shao Jianwen, Sha Yuebing
College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China.
Hangzhou Fangqian Technology Co., Ltd., Hangzhou 310030, China.
Sensors (Basel). 2025 Jul 27;25(15):4648. doi: 10.3390/s25154648.
In response to the demand for precise measurement of illuminance distribution in the quality control of LED monitoring fill light products and the iterative direction of secondary optical design, distributed photometric sensors have shown advantages, but their measurement uncertainty assessment faces challenges. This paper addresses the problem of uncertainty evaluation in photometric parameter measurement with a two-dimensional horizontal distributed photometric sensor and proposes an uncertainty evaluation framework for this task. We have established an uncertainty analysis model for the measurement system and provided two uncertainty synthesis methods, The Guide to the Expression of Uncertainty in Measurement and the Monte Carlo method. This study designed illuminance measurement experiments to validate the feasibility of the proposed uncertainty evaluation method. The results demonstrate that the actual probability distribution of the measurement data follows a trapezoidal distribution. Furthermore, the expanded uncertainty calculated using the GUM method was 21.1% higher than that obtained by the MCM. This work effectively addresses the uncertainty evaluation challenge for illuminance measurement tasks using a two-dimensional horizontal distributed photometric sensor. The findings offer valuable reference for the uncertainty assessment of other high-precision optical instruments and possess significant engineering value in enhancing the reliability of optical metrology systems.
针对LED监控补光产品质量控制中照度分布精确测量的需求以及二次光学设计的迭代方向，分布式光度传感器展现出优势，但其测量不确定度评估面临挑战。本文针对二维水平分布式光度传感器光度参数测量中的不确定度评估问题，提出了该任务的不确定度评估框架。我们建立了测量系统的不确定度分析模型，并提供了两种不确定度合成方法，即《测量不确定度表示指南》和蒙特卡洛方法。本研究设计了照度测量实验，以验证所提出的不确定度评估方法的可行性。结果表明，测量数据的实际概率分布呈梯形分布。此外，使用GUM方法计算的扩展不确定度比MCM方法获得的结果高21.1%。这项工作有效解决了使用二维水平分布式光度传感器进行照度测量任务的不确定度评估挑战。研究结果为其他高精度光学仪器的不确定度评估提供了有价值的参考，在提高光学计量系统的可靠性方面具有重要的工程价值。
