Using a lattice algorithm to estimate the Kalman gain vector in fast Newton-type adaptive filtering
نویسندگان
چکیده
In this paper we consider a recursive least squares (RLS) adaptive ltering problem where the input signal can be modelled as the output of a low order autoregressive (AR) process. We will show how a good estimate of the Kalman gain vector can be obtained using a small least squares lattice (LSL) lter. This estimate can then be used in the normal way to determine the optimum lter coe cients. The resulting adaptive ltering algorithm is similar in concept to the Fast Newton algorithm. The main di erence is the use of the LSL instead of a low order covariance domain fast RLS algorithm. The potential advantage of this new algorithm is that, unlike a covariance domain algorithm, a LSL can be implemented in a numerically stable form.
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