Reduced complexity recursive least square lattice structure adaptive filter by means of limited recursion of the backward and forward error prediction squares
First Claim
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1. A method for an m-stage recursive least squares lattice structure adaptive filter, the method comprising:
- receiving an input signal at an interface of the adaptive filter;
recursively updating filter coefficients for each stage of the adaptive filter with a processor operative in conjunction with stored data and instructions for each stage of the adaptive filter, includingreducing a number of updates of a forward error prediction squares and backward error prediction squares of the filter coefficients by performing the updates up to a predefined number of stages of the adaptive filter, andholding the forward error prediction squares and the backward error prediction squares constant during the remainder of the stages of the adaptive filter; and
producing a filtered signal as an output signal of the adaptive filter at an interface of the adaptive filter.
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Abstract
A method for reducing a computational complexity of an m-stage adaptive filter is provided by updating recursively forward and backward error prediction square terms for a first portion of a length of the adaptive filter, and keeping the updated forward and backward error prediction square terms constant for a second portion of the length of the adaptive filter.
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11 Claims
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1. A method for an m-stage recursive least squares lattice structure adaptive filter, the method comprising:
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receiving an input signal at an interface of the adaptive filter; recursively updating filter coefficients for each stage of the adaptive filter with a processor operative in conjunction with stored data and instructions for each stage of the adaptive filter, including reducing a number of updates of a forward error prediction squares and backward error prediction squares of the filter coefficients by performing the updates up to a predefined number of stages of the adaptive filter, and holding the forward error prediction squares and the backward error prediction squares constant during the remainder of the stages of the adaptive filter; and producing a filtered signal as an output signal of the adaptive filter at an interface of the adaptive filter. - View Dependent Claims (2, 3, 4)
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5. A method for reducing computational complexity of an in-stage adaptive filter, the method comprising:
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receiving an input signal at an input interface of the adaptive filter; filtering the input signal by a processor operative in conjunction with stored data and instructions for each stage of the adaptive filter, to produce a filtered signal, including updating recursively a forward error prediction square term for a first portion of a length of the adaptive filter; updating recursively a backward error prediction square term for the first portion of the length of the adaptive filter; and keeping the updated forward and backward error prediction square terms constant for a second portion of the length of the adaptive filter; and providing the filtered signal at an output interface of the adaptive filter. - View Dependent Claims (6, 7, 8)
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9. An m-stage adaptive filter comprising:
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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 reduce a number of updates of the forward error prediction squares and backward error prediction squares of the filter coefficients by performing the updates up to a predefined number of stages of the adaptive filter, and hold the forward error prediction squares and the backward error prediction squares constant during the remainder of the stages of the adaptive filter; and an interface to provide the filtered signal as an output signal. - View Dependent Claims (10, 11)
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Specification