Could someone explain how one arrives at the equation below for high pass filtering of the accelerometer values? I don\'t need mathematical derivation, just an intuitive interpr
While the other answers are correct, here is an simplistic explanation. With kFilteringFactor 0.1 you are taking 10% of the current value and adding 90% of the previous value. Therefore the value retains a 90% similarity to the previous value, which increases its resistance to sudden changes. This decreases noise but it also makes it less responsive to changes in the signal. To reduce noise and keep it responsive you would need non trivial filters, eg: Complementary, Kalman.