2. Do you have an underlying model that can define your process (a simple AR process can also be an answer)?
A good overview of Kalman filter can be found in 'Optimal state estimation' by Dan Simon.
You can also follow some of our recent publication where we have used Kalman filter to estimate the fundamental supply frequency of a signal and then used the estimate to remove it from the signal. This helped us in conditioning the signal by enhancing other components of the signal that were previously obscured by the fundamental component. Kalman filters have wide uses in optimal estimation, tracking, and numerous other applications.
2. Do you have an underlying model that can define your process (a simple AR process can also be an answer)?
A good overview of Kalman filter can be found in 'Optimal state estimation' by Dan Simon.
You can also follow some of our recent publication where we have used Kalman filter to estimate the fundamental supply frequency of a signal and then used the estimate to remove it from the signal. This helped us in conditioning the signal by enhancing other components of the signal that were previously obscured by the fundamental component. Kalman filters have wide uses in optimal estimation, tracking, and numerous other applications.
I am not sure what you are expecting to do with the kalman filter to the accelerometer but I presume the input to your system is acceleration (as read by the accelerometer) and you want to estimate position, velocity or both. There is a very good article in http://academic.csuohio.edu/simond/courses/eec644/kalman.pdf. This was Originally developed for use in spacecraft navigation, but it is useful for many applications. I hope if you give time to read this article it will help you to understand how to solve your problem.