FIR adaptive filter that uses delayed LMS
ha = adaptfilt.dlms(l,step,leakage,delay,errstates,coeffs,
ha = adaptfilt.dlms(l,step,leakage,delay,errstates,coeffs, constructs an FIR delayed LMS adaptive
For information on how to run data through your adaptive filter
object, see the Adaptive Filter Syntaxes section of the reference
Entries in the following table describe the input arguments
Adaptive filter length (the number of coefficients or
taps) and it must be a positive integer.
LMS step size. It must be a nonnegative scalar. You can
Your LMS leakage factor. It must be a scalar between
0 and 1. When
Update delay given in time samples. This scalar should
be a positive integer — negative delays do not work.
Vector of the error states of your adaptive filter. It
must have a length equal to the update delay (
Vector of initial filter coefficients. it must be a length
Vector of initial filter states for the adaptive filter.
It must be a length
In the syntax for creating the
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.
Defines the adaptive filter algorithm the object uses during adaptation
Vector of elements
Vector containing the initial filter coefficients. It
must be a length
Specifies the update delay for the adaptive algorithm.
Vector of zeros with the number of elements equal to
A vector comprising the error states for the adaptive filter.
Any positive integer
Reports the length of the filter, the number of coefficients or taps.
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.
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.
Sets the 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.
Vector of elements, data type double
Vector of the adaptive filter states.
System identification of a 32-coefficient FIR filter. Refer to the figure that follows to see the results of the adapting filter process.
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 mu = 0.008; % LMS step size. delay = 1; % Update delay ha = adaptfilt.dlms(32,mu,1,delay); [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'); grid on; xlabel('Coefficient #'); ylabel('Coefficient Value');
Using a delayed LMS adaptive filter in the process to identify an unknown filter appears to work as planned, as shown in this figure.
Shynk, J.J.,"Frequency-Domain and Multirate Adaptive Filtering," IEEE® Signal Processing Magazine, vol. 9, no. 1, pp. 14-37, Jan. 1992.