Sensor Data Cleaning: Filter Techniques

An easy way to smooth out the signal, eliminate noise, and facilitate acquiring from the sensor output is to filter it. This course discusses several types of filters and how to apply them to your design. We begin by investigating the moving average filter, which will eliminate higher-frequency information from the output. After that, you'll discover how to code it by writing various variables and equations and running tests to observe the outcomes.

Next, we explore other filtering methods, including averaging, running average and exponential filters. You'll discover the differences between them, such as which one is easier to implement, which requires more or less memory and how they operate. After that, we teach you how to use code lines and formulas to calculate the average, print the serial monitor, and initialise floats and other constants. Following that, you will learn how to compare the outcomes using graphs.

The low-pass filter and exponential moving algorithm (EMA), used to reduce potentiometer noise, are covered in the final section of the course. Then, we go over high-pass, band-pass, and band-stop filters and the whole programming procedure, including functions, variables, and equations unique to each. This course will instruct you how to convert filtering equations into actual code, compare the results, and make the proper decision when selecting the best filter for a given case. Register right away!