Filter Design Toolbox

adaptfilt.swftf

Construct a sliding window fast transversal least squares adaptive filter object

Syntax

• ha = 
  adaptfilt.swftf(l,delta,blocklen,gamma,gstates,dstates,coeffs,  
  states)
  

Description

ha = adaptfilt.swftf(l,delta,blocklen,gamma,gstates,dstates,...
coeffs,states) constructs a sliding window fast transversal least squares adaptive filter ha.

Input Arguments

Entries in the following table describe the input arguments for adaptfilt.swftf.

Input Argument	Description
l	Adaptive filter length (the number of coefficients or taps) and it must be a positive integer. l defaults to 10.
delta	Soft-constrained initialization factor. This scalar should be positive and sufficiently large to maintain stability. delta defaults to 1.
blocklen	Block length of the sliding window. This must be an integer at least as large as the filter length l, which is the default value.
gamma	Conversion factor. gamma defaults to the matrix [1 -1] that specifies soft-constrained initialization.
gstates	States of the kalman gain updates. gstates defaults to a zero vector of length (l + blocklen - 1).
dstates	Desired signal states of the adaptive filter. dstates defaults to a zero vector of length equal to (blocklen - 1). For a default object, dstates is (l-1).
coeffs	Vector of initial filter coefficients. It must be a length l vector. coeffs defaults to length l vector of all zeros.
states	Vector of initial filter states. states defaults to a zero vector of length equal to (l + blocklen - 2).

adaptfilt.swftf Object Properties

Since your adaptfilt.swftf filter is an object, it has properties that define its behavior in operation. Note that many of the properties are also input arguments for creating adaptfilt.swftf objects. To show you the properties that apply, this table lists and describes each property for the filter object.

Name	Range	Description
Algorithm	None	Defines the adaptive filter algorithm the object uses during adaptation
FilterLength	Any positive integer	Reports the length of the filter, the number of coefficients or taps
Coefficients	Vector of elements	Vector containing the initial filter coefficients. It must be a length l vector where l is the number of filter coefficients. coeffs defaults to length l vector of zeros when you do not provide the argument for input.
States	Vector of elements, data type double	Vector of the adaptive filter states. states defaults to a vector of zeros which has length equal to (l + projectord - 2).
BlockLength		Block length of the sliding window. This must be an integer at least as large as the filter length l, which is the default value.
InitFactor
KalmanGain		Empty when you construct the object, this gets populated after you run the filter.
ConversionFactor		Conversion factor. Called gamma when it is an input argument, it defaults to the matrix [1 -1] that specifies soft-constrained initialization.
FwdPrediction
BkwdPredictions
DesiredSignalStates		Desired signal states of the adaptive filter. dstates defaults to a zero vector with length equal to (blocklen - 1).
KalmanGainStates		Contains the states of the Kalman gains for the adaptive algorithm. Initialized to a vector of double data type entries.
ResetBeforeFiltering	off or on	Determine whether the filter states get restored to their starting values for each filtering operation. The starting values are the values in place when you create the filter if you have not changed the filter since you constructed it. ResetBeforeFiltering returns to zero any state that the filter changes during processing. States that the filter does not change are not affected. Defaults to 'on'.
NumSamplesProcessed	Any integer	Returns the number of samples processed during filtering. Defaults to zero.

Examples

Over 500 iterations, perform a system identification of a 32-coefficient FIR filter.

• x  = randn(1,500);     % Input to the filter
  b  = fir1(31,0.5);     % FIR system to be identified
  n  = 0.1*randn(1,500); % Observation noise signal
  d  = filter(b,1,x)+n;  % Desired signal
  L  = 32;               % Adaptive filter length
  del = 0.1;             % Soft-constrained initialization factor
  N  = 64;               % block length
  ha = adaptfilt.swftf(L,del,N);
  [y,e] = filter(ha,x,d);
  subplot(2,1,1); plot(1:500,[d;y;e]);
  title('System Identification of an FIR filter');
  legend('Desired','Output','Error');
  xlabel('Time Index'); ylabel('Signal Value');
  subplot(2,1,2); stem([b.',ha.Coefficients.']);
  legend('Actual','Estimated'); 
  xlabel('Coefficient #'); ylabel('Coefficient Value'); grid on;
  

See Also

adaptfilt.ftf, adaptfilt.swrls, adaptfilt.ap, adaptfilt.apru

References

D.T.M. Slock and Kailath, T., "A Modular Prewindowing Framework for Covariance FTF RLS Algorithms," Signal Processing, vol. 28, pp. 47-61, 1992

D.T.M. Slock and Kailath, T., "A Modular Multichannel Multi-Experiment Fast Transversal Filter RLS Algorithm," Signal Processing, vol. 28, pp. 25-45, 1992

adaptfilt.ss

adaptfilt.swrls