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I'm not sure if this goes here, but I'm having a bit of trouble understanding the Kalman filter. What I want to do is condition a sensor with a PID compensator to find the optimal gains for the PID filter. This is a sort of pole balancing problem with video. I was wondering if someone could give a good explanation of the basics of the Kalman filter.
Thanks in advance!
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Kalman filters are used to optimally estimate the variables of interests when they can't be measured directly, but an indirect measurement is available. They are also used to find the best estimate of states by combining measurements from various sensors in the presence of noise.
The control kicks in after you've determined the error. So the Kalman Filter is not really a control system. Save this answer.
Track a Single Object Using Kalman FilterCreate vision. KalmanFilter by using configureKalmanFilter. Use predict and correct methods in a sequence to eliminate noise present in the tracking system. Use predict method by itself to estimate ball's location when it is occluded by the box.
Particle Filter Particle FIlters can be used in order to solve non-gaussian noises problems, but are generally more computationally expensive than Kalman Filters. That's because Particle Filters uses simulation methods instead of analytical equations in order to solve estimation tasks.
The most human readable intro with examples I have found so far is the SIGGRAPH Course Pack.
I am not sure I understand what you are trying to do. It seems to me you are trying to tune your PID controller but you do not need Kalman filter for that.
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