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dsp.AdaptiveLatticeFilter

Adaptive lattice filter

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Description

Thedsp.AdaptiveLatticeFilterSystem object™ computes output, error, and coefficients using a lattice-based FIR adaptive filter.

To implement the adaptive FIR filter object:

  1. Create thedsp.AdaptiveLatticeFilterobject and set its properties.

  2. Call the object with arguments, as if it were a function.

To learn more about how System objects work, seeWhat Are System Objects?

Creation

Syntax

alf = dsp.AdaptiveLatticeFilter
alf = dsp.AdaptiveLatticeFilter(len)
alf = dsp.AdaptiveLatticeFilter(Name,Value)

Description

alf= dsp.AdaptiveLatticeFilterreturns a lattice-based FIR adaptive filter System object,alf. This System object computes the filtered output and the filter error for a given input and desired signal.

example

alf= dsp.AdaptiveLatticeFilter(len)returns anAdaptiveLatticeFilterSystem object with theLengthproperty set tolen.

example

alf= dsp.AdaptiveLatticeFilter(Name,Value)returns anAdaptiveLatticeFilterSystem object with each specified property set to the specified value. Enclose each property name in single quotes. Unspecified properties have default values.

Properties

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Unless otherwise indicated, properties arenontunable, which means you cannot change their values after calling the object. Objects lock when you call them, and thereleasefunction unlocks them.

If a property istunable, you can change its value at any time.

For more information on changing property values, seeSystem Design in MATLAB Using System Objects.

Specify the method used to calculate filter coefficients as one of'Least-squares Lattice','QR-decomposition Least-squares Lattice',“梯度自适应格'. The default value is'Least-squares Lattice'. For algorithms used to implement these three different methods, refer to[1][2]. This property is nontunable.

Specify the length of the FIR filter coefficients vector as a positive integer value. This property is nontunable.

Data Types:single|double|int8|int16|int32|int64|uint8|uint16|uint32|uint64

Specify the least-squares lattice forgetting factor as a scalar positive numeric value less than or equal to 1. Setting this value to 1 denotes infinite memory during adaptation.

可调:Yes

Dependencies

This property applies only if the Method property is set to'Least-squares Lattice'or'QR-decomposition Least-squares Lattice'.

Data Types:single|double|int8|int16|int32|int64|uint8|uint16|uint32|uint64

Specify the joint process step size of the gradient adaptive lattice filter as a positive numeric scalar less than or equal to 1.

可调:Yes

Dependencies

This property applies only if theMethodproperty is set to“梯度自适应格'.

Data Types:single|double|int8|int16|int32|int64|uint8|uint16|uint32|uint64|logical

Specify an offset value for the denominator of theStepSizenormalization term as a nonnegative numeric scalar. A nonzero offset helps avoid a divide-by-near-zero condition when the input signal amplitude is very small.

可调:Yes

Dependencies

This property applies only if theMethodproperty is set to“梯度自适应格'.

Data Types:single|double|int8|int16|int32|int64|uint8|uint16|uint32|uint64

Specify the reflection process step size of the gradient adaptive lattice filter as a scalar numeric value between 0 and 1, both inclusive. The default value is theStepSizeproperty value.

可调:Yes

Dependencies

Use this property only if theMethodproperty is set to“梯度自适应格'.

Data Types:single|double|int8|int16|int32|int64|uint8|uint16|uint32|uint64|logical

Specify the averaging factor as a positive numeric scalar less than 1. Use this property to compute the exponentially windowed forward and backward prediction error powers for the coefficient updates. The default is the value of1 - StepSize.

可调:Yes

Dependencies

This property applies only if theMethodproperty is set to“梯度自适应格.

Data Types:single|double

Specify the initial values for the prediction error vectors as a scalar positive numeric value.

If theMethodproperty is set to'Least-squares Lattice'or'QR-decomposition Least-squares Lattice', default value is1.0. If theMethodproperty is set to“梯度自适应格', default value is0.1.

可调:Yes

Data Types:single|double

Specify the initial values of the FIR adaptive filter coefficients as a scalar or a vector of length equal to the value of theLengthproperty.

可调:Yes

Data Types:single|double|int8|int16|int32|int64|uint8|uint16|uint32|uint64

Specify whether to lock the filter coefficient values. By default, the value of this property isfalse, and the object continuously updates the filter coefficients. If this property is set totrue, the filter coefficients do not update and their values remain the same.

可调:Yes

Dependencies

This property is applicable only if theMethodproperty is set to“梯度自适应格'.

Usage

Syntax

[y,err] = alf(x,d)

Description

example

[y,err] = alf(x,d)filters the inputx, usingdas the desired signal, and returns the filtered output inyand the filter error inerr. The System object estimates the filter weights needed to minimize the error between the output signal and the desired signal. You can access these coefficients by accessing theCoefficientsproperty of the object. This can be done only after calling the object. For example, to access the optimized coefficients of thealffilter, callalf.Coefficientsafter you pass the input and desired signal to the object.

Input Arguments

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The signal to be filtered by the adaptive lattice filter. The input,x, and the desired signal,d,必须有相同的大小和数据类型。

The input can be a variable-size signal. You can change the number of elements in the column vector even when the object is locked. The System object locks when you call the object.

Data Types:single|double
Complex Number Support:Yes

The adaptive lattice filter adapts its coefficients to minimize the error,err, and converge the input signalxto the desired signaldas closely as possible.

The input,x, and the desired signal,d,必须有相同的大小和数据类型。

The desired signal can be a variable-size signal. You can change the number of elements in the column vector even when the object is locked. The System object locks when you call the object.

Data Types:single|double
Complex Number Support:Yes

Output Arguments

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Filtered output, returned as a scalar or a column vector. The object adapts its filter coefficients to converge the input signalxto match the desired signald. The filter outputs the converged signal.

Data Types:single|double
Complex Number Support:Yes

Difference between the output signalyand the desired signald, returned as a scalar or a column vector. The objective of the adaptive lattice filter is to minimize this error. The object adapts its coefficients to converge towards optimal filter coefficients that produce an output signal that matches closely with the desired signal. To access the adaptive lattice filter coefficients, callalf.Coefficientsafter you pass the input and desired signal to the object algorithm.

Data Types:single|double
Complex Number Support:Yes

Object Functions

To use an object function, specify the System object as the first input argument. For example, to release system resources of a System object namedobj, use this syntax:

release(obj)

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msesim Estimated mean squared error for adaptive filters
step RunSystem objectalgorithm
release Release resources and allow changes toSystem objectproperty values and input characteristics
reset Reset internal states ofSystem object

Examples

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Open Live Script

Note: If you are using R2016a or an earlier release, replace each call to the object with the equivalentstepsyntax. For example,obj(x)becomesstep(obj,x).

Create the QPSK signal and the noise, filter them to obtain the received signal, and delay the received signal to obtain the desired signal.

D = 16; b = exp(1i*pi/4)*[-0.7 1]; a = [1 -0.7]; ntr = 1000; s = sign(randn(1,ntr+D)) + 1i*sign(randn(1,ntr+D)); n = 0.1*(randn(1,ntr+D) + 1i*randn(1,ntr+D)); r = filter(b,a,s) + n; x = r(1+D:ntr+D); d = s(1:ntr);

Use the Adaptive Lattice Filter to compute the filtered output and the filter error for the input and desired signal.

lam = 0.995; del = 1; alf = dsp.AdaptiveLatticeFilter('Length', 32,...'ForgettingFactor'林,'InitialPredictionErrorPower', del); [y,e] = alf(x,d);

Plot the In-Phase and the Quadrature components of the desired, output, and the error signals.

subplot(2,2,1); plot(1:ntr,real([d;y;e])); title('In-Phase Components'); legend('Desired','Output','Error'); xlabel('time index'); ylabel('signal value'); subplot(2,2,2); plot(1:ntr,imag([d;y;e])); title('Quadrature Components'); legend('Desired','Output','Error'); xlabel('time index'); ylabel('signal value');

Figure contains 2 axes objects. Axes object 1 with title In-Phase Components contains 3 objects of type line. These objects represent Desired, Output, Error. Axes object 2 with title Quadrature Components contains 3 objects of type line. These objects represent Desired, Output, Error.

Plot the received and equalized signals’ scatter plots.

subplot(2,2,3); plot(x(ntr-100:ntr),'.'); axis([-3 3 -3 3]); title('Received Signal Scatter Plot'); axis('square'); xlabel('Real[x]'); ylabel('Imag[x]'); gridon; subplot(2,2,4); plot(y(ntr-100:ntr),'.'); axis([-3 3 -3 3]); title('Equalized Signal Scatter Plot'); axis('square'); xlabel('Real[y]'); ylabel('Imag[y]'); gridon;

Figure contains 4 axes objects. Axes object 1 with title In-Phase Components contains 3 objects of type line. These objects represent Desired, Output, Error. Axes object 2 with title Quadrature Components contains 3 objects of type line. These objects represent Desired, Output, Error. Axes object 3 with title Received Signal Scatter Plot contains an object of type line. Axes object 4 with title Equalized Signal Scatter Plot contains an object of type line.

Open Live Script

Note: If you are using R2016a or an earlier release, replace each call to the object with the equivalentstepsyntax. For example,obj(x)becomesstep(obj,x).

ha = fir1(31,0.5);% FIR system to be identifiedfir = dsp.FIRFilter('Numerator',ha); iir = dsp.IIRFilter('Numerator',sqrt(0.75),...'Denominator',[1 -0.5]); x = iir(sign(randn(2000,25)));% Observation noise signaln = 0.1*randn(size(x));% Desired signald = fir(x)+n;% Filter lengthl = 32;% Decimation factor for analysis% and simulation resultsm = 5; ha = dsp.AdaptiveLatticeFilter(l); [simmse,meanWsim,Wsim,traceKsim] = msesim(ha,x,d,m); plot(m*(1:length(simmse)),10*log10(simmse)); xlabel('Iteration'); ylabel('MSE (dB)');% Plot the learning curve used for% adaptive lattice filter used in system identificationtitle('Learning curve')

Figure contains an axes object. The axes object with title Learning curve contains an object of type line.

References

[1] Griffiths, Lloyd J. “A Continuously Adaptive Filter Implemented as a Lattice Structure”.Proceedings of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing,Hartford, CT, pp. 683–686, 1977 .

[2]Haykin, S.Adaptive Filter Theory, 4th Ed. Upper Saddle River, NJ: Prentice Hall, 1996.

Extended Capabilities

See Also

Objects

Introduced in R2013b

DSP System Toolbox Documentation

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