Where 'R^-1' is the inverse of the auto correlation matrix, 'K' is a very large number and 'I' is Identity matrix. Why does 'K' have to be very large?
Well, sometimes K is set large. But, the recursion for R-1 is obtained by the algebraic Riccati equation. So, K must be close to real value in order to reduce errors caused by transients. The best way is to allow some data, investigate R, and estimate K before running the RLS algorithm.
Do not forget that the RLS are applicable to stationary and quasi stationary processes. In the case of brightly pronounced dynamics, use the Kalman algorithm.
If you do not know the noise statistics and initial error statistics, use the unbiased FIR algorithm. This algorithm also has the RLS or Kalman form, but is more robust.
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