[…] The code provided in this tutorial is simple, possibly too simple for this application. The sensor used is unreliable, making the system excessively sensitive to temperature small oscillations and noise. A better approach would be to take repeated samples over a longer period of time. Averaging them reduces the effect of noise on the final measure. If you want an optimal solution, however, you can use a Kalman filter. It’s a powerful tool that allows to attenuate and to remove noise from sensors. To read more about it, check the tutorial A Gentle Introduction to Kalman Filters. […]
[…] jumpy and unreliable. An interesting approach to remove noise from your readings is by adopting a Kalman filter, which has been discussed in details in a […]
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[…] The code provided in this tutorial is simple, possibly too simple for this application. The sensor used is unreliable, making the system excessively sensitive to temperature small oscillations and noise. A better approach would be to take repeated samples over a longer period of time. Averaging them reduces the effect of noise on the final measure. If you want an optimal solution, however, you can use a Kalman filter. It’s a powerful tool that allows to attenuate and to remove noise from sensors. To read more about it, check the tutorial A Gentle Introduction to Kalman Filters. […]
[…] jumpy and unreliable. An interesting approach to remove noise from your readings is by adopting a Kalman filter, which has been discussed in details in a […]