FIR adaptive filter that uses FFTbased BLMS
adaptfilt.blmsfft
has been removed. Use dsp.LMSFilter
, dsp.BlockLMSFilter
, dsp.RLSFilter
, dsp.FastTransversalFilter
, dsp.AffineProjectionFilter
, dsp.AdaptiveLatticeFilter
, dsp.FilteredXLMSFilter
, dsp.FrequencyDomainAdaptiveFilter
,
or dsp.AffineProjectionFilter
instead.
ha = adaptfilt.blmsfft(l,step,leakage,blocklen,coeffs,
states)
ha = adaptfilt.blmsfft(l,step,leakage,blocklen,coeffs,
constructs an FIR block LMS adaptive
filter object
states)ha
where l
is
the adaptive filter length (the number of coefficients or taps) and
must be a positive integer. l
defaults to 10. step
is
the block LMS step size. It must be a nonnegative scalar. The function maxstep
may
be helpful to determine a reasonable range of step size values for
the signals you are processing. step
defaults to
0.
leakage
is the block LMS leakage factor.
It must also be a scalar between 0 and 1. When leakage
is
less than one, the adaptfilt.blmsfft
implements
a leaky block LMS algorithm. leakage
defaults to
1 (no leakage). blocklen
is the block length used.
It must be a positive integer such that
blocklen + length(coeffs)
is a power of two; otherwise, an adaptfilt.blms
algorithm
is used for adapting. Larger block lengths result in faster execution
times, with poor adaptation characteristics as the cost of the speed
gained. blocklen
defaults to l
.
Enter your initial filter coefficients in coeffs
,
a vector of length l
. When omitted, coeffs
defaults
to a length l
vector of all zeros. states
contains
a vector of initial filter states; it must be a length l
vector. states
defaults
to a length l
vector of all zeros when you omit
the states
argument in the calling syntax.
For information on how to run data through your adaptive filter
object, see the Adaptive Filter Syntaxes section of the reference
page for filter
.
In the syntax for creating the adaptfilt
object,
the input options are properties of the object you create. This table
lists the properties for the block LMS object, their default values,
and a brief description of the property.
Property  Default Value  Description 

 None  Defines the adaptive filter algorithm the object uses during adaptation 
 Any positive integer  Reports the length of the filter, the number of coefficients or taps 
 Vector of elements  Vector containing the initial filter coefficients. It
must be a length 
 Vector of elements of length  Vector of the adaptive filter states. 
 1  Specifies the leakage parameter. Allows you to implement a leaky algorithm. Including a leakage factor can improve the results of the algorithm by forcing the algorithm to continue to adapt even after it reaches a minimum value. Ranges between 0 and 1. 
 Vector of length  Size of the blocks of data processed in each iteration 
 0.1  Sets the block LMS algorithm step size used for each
iteration of the adapting algorithm. Determines both how quickly and
how closely the adaptive filter converges to the filter solution.
Use 

 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. 
Identify an unknown FIR filter with 32 coefficients using 512 iterations of the adapting algorithm.
x = randn(1,512); % Input to the filter b = fir1(31,0.5); % FIR system to be identified no = 0.1*randn(1,512); % Observation noise signal d = filter(b,1,x)+no; % Desired signal mu = 0.008; % Step size n = 16; % Block length ha = adaptfilt.blmsfft(32,mu,1,n); [y,e] = filter(ha,x,d); subplot(2,1,1); plot(1:500,[d(1:500);y(1:500);e(1:500)]); 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'); grid on; xlabel('Coefficient #'); ylabel('Coefficient Value');
As a result of running the adaptation process, filter object ha
now
matches the unknown system FIR filter b
, based
on comparing the filter coefficients derived during adaptation.
Shynk, J.J., "FrequencyDomain and Multirate Adaptive Filtering," IEEE^{®} Signal Processing Magazine, vol. 9, no. 1, pp. 1437, Jan. 1992.