Reduced complexity recursive least square lattice structure adaptive filter by means of estimating the backward and forward error prediction squares using binomial expansion

1. A method for reducing computational complexity of an m-stage adaptive filter, the method comprising:

receiving an input signal-to be filtered;

developing a set of filter coefficients for the received input signal;

expanding a weighted sum of forward prediction error squares into a corresponding binomial expansion series;

expanding a weighted sum of backward prediction error squares into a corresponding binomial expansion series;

determining coefficient updates of the adaptive filter with the weighted sum of forward and backward prediction error squares approximated by a select number of terms of their corresponding binomial expansion series;

filtering the received input signal in accordance with the updated coefficients to produce a filtered signal; and

providing the filtered signal as an output signal.

2. The method of claim 1 further comprising:

pre-calculating a constant weighting factor; and

updating the weighted sum of forward and backward prediction error squares using the pre-calculated constant weighting factor.

3. The method of claim 2 further comprising:

selecting a value of the constant weighting factor greater than 0.999; and

using a single term of the corresponding binomial expansion series of the forward and backward prediction error squares when determining the coefficient updates of the adaptive filter.

4. A method for an m-stage recursive least squares lattice structure adaptive filter, the method comprising:

receiving an input signal to be filtered at an input of the adaptive filter;

developing a set of filter coefficients for the received input signal;

updating the set of filter coefficients by estimating an expansion of Taylor's theorem within a given region of convergence;

filtering the input signal using the updated set of filter coefficients; and

producing a filtered signal at an output of the adaptive filter.

5. The method of claim 4 wherein updating the set of filter coefficients comprises:

estimating a forward error prediction square term using a first Taylor series expansion; and

estimating a backward error prediction square term using a second Taylor series expansion.

6. The method of claim 5 further comprising:

pre-calculating a constant weighting factor; and

estimating the forward error prediction square term and the backward error prediction square term using the pre-calculated constant weighting factor.

7. The method of claim 6 further comprising:

selecting a value of the constant weighting factor greater than 0.999 so that a single term of the first Taylor series expansion or the second Taylor series expansion may be used for estimating the forward error prediction square term and the backward error prediction square term.

8. An adaptive filter comprising:

an interface to receive an input signal;

a processor operative in conjunction with stored data and instructions to recursively update filter coefficients for each stage of the adaptive filter while producing a filtered signal, the processor configured to updating the set of filter coefficients by estimating an expansion of Taylor's theorem within a given region of convergence; and

an interface to provide the filtered signal as an output signal.

9. The adaptive filter of claim 8 wherein the processor is operative to

estimate a forward error prediction square term using a Taylor series expansion; and

estimate a backward error prediction square term using a Taylor series expansion.