Quantcast

Documentation Center

  • Trial Software
  • Product Updates

Contents

adaptfilt.swftf

FIR adaptive filter that uses sliding window fast transversal least squares

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.

For information on how to run data through your adaptive filter object, see the Adaptive Filter Syntaxes section of the reference page for filter.

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).

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

BkwdPredictions

 

Returns the predicted samples generated during adaptation. Refer to [2] in the bibliography for details about linear prediction.

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.

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.

ConversionFactor

 

Conversion factor. Called gamma when it is an input argument, it defaults to the matrix [1 -1] that specifies soft-constrained initialization.

DesiredSignal States

 

Desired signal states of the adaptive filter. dstates defaults to a zero vector with length equal to (blocklen - 1).

FilterLength

Any positive integer

Reports the length of the filter, the number of coefficients or taps

FwdPrediction

 

Contains the predicted values for samples during adaptation. Compare these to the actual samples to get the error and power.

InitFactor

 

Soft-constrained initialization factor. This scalar should be positive and sufficiently large to prevent an excessive number of Kalman gain rescues. delta defaults to one.

KalmanGain

 

Empty when you construct the object, this gets populated after you run the filter.

KalmanGainStates

 

Contains the states of the Kalman gains for the adaptive algorithm. Initialized to a vector of double data type entries.

PersistentMemory

false or true

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. PersistentMemory returns to zero any state that the filter changes during processing. States that the filter does not change are not affected. Defaults to false.

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).

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;

Review the figure for the results of the example. When you evaluate the example you should get the same results, within the differences in the random noise signal you use.

References

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

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

See Also

| | |

Was this topic helpful?