There is interrelationship between process noise and measurement noise. The 
averaging constant K is given as:

K =  (Estim. Error + Process Noise)/ [(Estim. Error + Process Noise) + 
Measurement Noise Variance]
 
When measurement noise is much smaller than the observation noise, K= 1 and 
there is no averaging. When measurement noise is much greater, K is very small 
and averaging is very significant. Of course, one more cog in the wheel is 
Estimation Error, which is recomputed at each time step, making Kalman 
adaptive, 
unlike EMA, - where K is constant. Estimation Error is measure of how well 
the algorithm suppresses noise.
 
 
On a different topic...
 
In KalmanTest:
 
addParam(FAST_ERROR, 0, 100, 1, 0); 
 
If the last parameter is indeed the value used for back test, it is probably 
best to make it very small but not zero. I think setting the value of 
short-term 
noise to zero can do strange things to an algorithm designed to suppress 
that noise.

________________________________
From: nonlinear5 <[email protected]>
To: JBookTrader <[email protected]>
Sent: Tue, November 30, 2010 8:49:17 AM
Subject: [JBookTrader] Re: Status of Kalman filter?

I noticed that there is also kalman.setProcess_noise_cov(), in
addition to the kalman.setMeasurement_noise_cov() which I am currently
using. I replaced setMeasurement_noise_cov with
setProcess_noise_cov(), and now the Tension indicator much more
closely resembles the original Tension where EMA was used as a
filter.

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