Computationally efficient algorithms for third order adaptive Volterra filters
نویسندگان
چکیده
The input autocorrelation matrix for a third order (cubic) Volterra adaptive filter for general colored Gaussian input processes is analyzed to determine how to best formulate a computationally efftcient fast adaptive algorithm. When the input signal samples are ordered properly within the input data vector, the autocorrelation matrix of the cubic filter inherits a block diagonal structure, with some of the sub-blocks also having diagonal structure. A computationally efftcient adaptive algorithm is presented that takes advantage of the spar&y and unique structure of the correlation matrix that results from this formulation.
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