Sensor fusion applied to the estimate of luminous intensity (LUX) in practical class

• Autor(a) principal: Acorsi, Matheus Gabriel
• Data de Publicação: 2023
• Outros Autores: Silva, Thiago Lima da, Regazzo, Jamile Raquel, Tabile, Rubens André, Baesso, Murilo Mesquita, Gimenez, Leandro Maria
• Tipo de documento: Artigo
• Idioma: por; eng
• Título da fonte: Diversitas Journal
• Texto Completo: https://diversitasjournal.com.br/diversitas_journal/article/view/2582
• topic: instrumentação; modelagem matemática; dados quantitativos redundantes; instrumentation; mathematical modeling; redundant quantitative data

In the last ten years, the development of sensors with greater accuracy and precision due to improvements in manufacturing processes has enabled the expansion of their use in several areas. However, the purchase price, mainly of products from renowned manufacturers, in view of their applications, can make simpler projects unfeasible. The sensor data fusion technique is a viable alternative to resolve this issue, as mathematical models can be proposed and used in different situations. These models allow improving the data obtained in order to generate reliable information. Therefore, the objective of this work was to verify the performance of multiple linear regression applied to the fusion of redundant quantitative data from 5mm LDR sensors in estimating the luminous intensity (LUX) in simulated scenarios. To carry out the experiment, 3 LDR (Light Dependent Resistor) sensors, 3 LM393 signal conditioners, 1 USB 6009 DAQ data acquisition board (14 bits), 1 LT40 Extech luxmeter, in addition to the LabView software were used. It was found that LDR A and B sensors showed higher levels of accuracy. Furthermore, a significant improvement in the level of accuracy was found when combining data from sensors A and B in the form of multiple linear regression.