<![CDATA[Light intensity measurements based on sensors often experience signal quality degradation due to noise interference. Although filtering methods are commonly used to reduce noise, improvements in signal stability are frequently accompanied by changes in dynamic response, resulting in a trade-off between accuracy and response speed that has not been extensively analyzed. This study aims to quantitatively evaluate the effect of Moving Average Filter (MAF) parameters on this trade-off in light sensor systems. The proposed method employs a first-order system simulation with a step input signal contaminated by White Gaussian Noise, which is subsequently processed using the MAF with various window sizes ( = 3, 5, 10, 20, and 40). The evaluation is conducted using Root Mean Square Error (RMSE) and rise time as indicators of estimation accuracy and response speed, respectively. The results demonstrate that increasing the window size significantly reduces the RMSE, decreasing from 0.0156 to 0.0072 under step-up conditions and from 0.0132 to 0.0066 under step-down conditions. Optimal performance is observed within the range of = 10–20. However, this improvement in accuracy is accompanied by an increase in rise time, from 0.1027 s to 0.1175 s for step-up conditions and from 0.1036 s to 0.1191 s for step-down conditions, indicating a slower dynamic response. These findings confirm the existence of a trade-off between signal accuracy and response speed. Therefore, this study provides a quantitative basis for determining optimal filter parameters by considering the balance between measurement accuracy and system responsiveness in light intensity sensing applications.]]>
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