Research on the Method of Light Intensity Detection based on Kalman Filtering

Abstract

A light intensity detection algorithm using Kalman filtering is proposed for the usage of data fusion in multi-sensor scenarios, like industrial control, machine vision, and navigation systems. In traditional measurements, the result is often faced with noise interference, causing accuracy problems. This algorithm Kalman filtering can be a good solution to noise signal reduction and provide optimal estimation in various domains that need prediction. As Kalman filtering is suitable for light intensity measurements, This work experimented with the steps shown below. First, the light intensity data is reported by sensors, and the mean and standard deviation of the measurement data are calculated; Second, by introducing a Kalman filtering model into the detecting model, the precision of estimating is improved, and the reliability of the system is enhanced; Finally, the data and diagram are exported by Matlab, and the Kalman filtering algorithm is compared with classical algorithms like moving average, median filtering. The experimental data shows Kalman filtering can be better applied to state estimation and signal processing domains, and the average light intensity data is 949.744. This result supports that Kalman filtering provides a robust and efficient method and it bed-covers the future research in improving the precision in light intensity measurements.