| 摘要 |
An adaptive filter is implemented by a computer ( 10 ) processing an input signal using a recursive least squares lattice (RLSL) algorithm ( 12 ) to obtain forward and backward least squares prediction residuals. A prediction residual is the difference between a data element in a sequence of elements and a prediction of that element from other sequence elements. Forward and backward residuals are converted at ( 14 ) to interpolation residuals which are unnormalized Kalman gain vector coefficients. Interpolation residuals are normalized to produce the Kalman gain vector at ( 16 ). The Kalman gain vector is combined at ( 18 ) with input and reference signals x(t) and y(t), which provides updates for the filter coefficients or weights to reflect these signals as required to provide adaptive filtering.
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