13 init_ rlslattice

Purpose

Creates and initializes the variables required for the RLS-Lattice Joint Process Estimator using the a posteriori estimation errors.

Syntax

[ff,bb,fb,be,cf,b,d,y,e,kf,kb,c] = init_rlslattice(L)
[ff,bb,fb,be,cf,b,d,y,e,kf,kb,c] = init_rlslattice(L,ff0,
bb0,fb0,be0,cf0,b0,d0)

Description

Figure 5.19: Block diagram of the RLS-LATTICE adaptive Joint Process Estimator.

The variables of the RLS LATTICE are summarized below (see Fig. 5.19).

Input Parameters [Size]:: 
  L   : Linear combiner length
  ff0 : initial autocorr. of forward prediction error [Lx1]
  bb0 : initial autocorr. of backward prediction error [Lx1]
  fb0 : initial crosscorrelation of f and b [Lx1]
  be0 : initial crosscorrelation of b and e [Lx1]
  cf0 : initial conversion factor [Lx1]
  b0  : initial vector of backward prediction error [Lx1]
  d0  : initial desired response [1x1]
Output parameters [Default]::
  ff  : autocorr. of forward prediction error [.001*ones(L,1)]
  bb  : autocorr. of backward prediction error [.001*ones(L,1)]
  fb  : crosscorrelation of f and b [zeros]
  be  : crosscorrelation of b and e [zeros]
  cf  : conversion factor [ones]
  b   : vector of backward prediction error [zeros]
  d   : desired output [rand]
  y   : Linear combiner output [c' * b]
  e   : Linear combiner error [d-y]
  kf  : forward lattice coefficients [zeros]
  kb  : backward lattice coefficients [zeros]
  c   : linear combiner coefficients [zeros]

Example

L  = 5;                 % Number of lattice stages 
ff = 0.001*ones(L,1);   % autocorr. for forward prediction error
bb = 0.001*ones(L,1);   % autocorr. for backward prediction error
fb = zeros(L,1);        % crosscorr. between f and b
be = zeros(L,1);        % crosscorr. between b and e
cf = zeros(L,1);        % conversion factor
b  = zeros(L,1);        % backward prediction error 
d  = .22;               % initial desired sample

% Create and initialize an RLS lattice filter
[ff,bb,fb,be,cf,b,d,y,e,kf,kb,c] = init_rlslattice(L,ff,...
                                   bb,fb,be,cf,b,d);

Remarks

• Supports both real and complex signals and filters.
• Use input parameters 2 through 8 to initialize the algorithm storage. This is helpful when the adaptation process is required to start from a known operation point calculated off-line or from previous simulations.

See Also

ASPTRLSLATTICE, MODEL_ LMSLATTICE, ASPTRLSLATTICE2.