SPcovFilt

Routine

double SPcovFilt (double Ed, const float *R[], const float r[], float h[], int N)

Purpose

Find filter coefficients using the covariance approach

Description

         N-1
  y(k) = SUM h(i) x(k-D(i)) ,
         i=0

  e(k) = d(k) - y(k) ,

where d(k) is the desired signal. To minimize the mean-square filtering error, solve

  R h = r,

where R is the symmetric positive definite covariance matrix, h is the vector of filter coefficients and r is a vector of correlation values. The matrix R and and vector r are defined as follows

  R(i,j) = E[x(k-D(i)) x(k-D(j))],  for 0 <= i,j < N,
    r(i) = E[x(k) d(k-D(i))],       for 0 <= i < N.

The solution is determined using a Cholesky decomposition of the matrix R. The resulting mean-square filtering error can be expressed as

  ferr = Ed - 2 h'r + h'R h
       = Ed - h'r ,

where Ed is the mean-square value of the desired signal,

  Ed = E[d(k)^2] .

The expectation operator E[.] is often replaced by a sum over k over an interval, say 0 <= k < Nk. Minimization of the prediction error over this finite interval defines the so-called covariance method for determining the filter coefficients. If the filter delays are of the form D(i) = Nd + i,

           Nk-1
  R(i,j) = SUM x(k-i-Nd) x(k-j-Nd) .
           k=0

         Nk-1
  r(i) = SUM x(k) x(k-i-Nd) .
         k=0

If the coefficient matrix is numerically not positive definite, or if the error energy becomes negative at some stage in the calculation, the remaining filter coefficients are set to zero.

Parameters

<- double SPcovFilt

Resultant filter mean-square error

-> double Ed

Signal energy for the desired signal. This value is used only for the computation of the mean-square error.

-> const float *R[]

R is an array of pointers to the rows of an N by N positive definite correlation matrix. Only the lower triangular portion of R is accessed. Note that with ANSI C, if the actual parameter is not declared to have the const attribute, an explicit cast to (const float **) is required.

-> const float r[]

Vector of cross-correlations (N elements). The vector r can be the same vector as h.

<- float h[]

Vector of filter coefficients (N values)

-> int N

Number of filter coefficients

Author / revision

See Also