Our teachers around me here are researching on filters. According to their logic, a signal with some normally distributed noise is just good, ha-ha! Is it really that the IR proximity sensor’s noise is uniformly distributed but not normally distributed?
If step by step, the noise is not relevant, the discrete Kalman filter works fine! But, the conclusion may seem different with some normally distributed noise.
In your:
classdef ProximitySensor < simiam.ui.Drawable
…..
function update_range(obj, distance)
….
variance = 0.005;
noise = -variance+2_variance_rand();
I think randn() generate normally distributed pseudonym numbers.
Some students study on filters love play with different noise! I think if give them an interface in your Simulink blocks may let them feel better.
jdelacroix
commented
10 years ago
Our teachers around me here are researching on filters. According to their logic, a signal with some normally distributed noise is just good, ha-ha! Is it really that the IR proximity sensor’s noise is uniformly distributed but not normally distributed? If step by step, the noise is not relevant, the discrete Kalman filter works fine! But, the conclusion may seem different with some normally distributed noise.
In your: classdef ProximitySensor < simiam.ui.Drawable ….. function update_range(obj, distance) …. variance = 0.005; noise = -variance+2_variance_rand();
I think randn() generate normally distributed pseudonym numbers. Some students study on filters love play with different noise! I think if give them an interface in your Simulink blocks may let them feel better.