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adaptive filters -凯发k8网页登录

lms, rls, frequency domain filters, affine projection filter, adaptive lattice filter

dsp system toolbox™ offers several variations of the lms and rls adaptive finite impulse response (fir) filter algorithms. while these algorithms differ in detail, they share a common operational approach, which is to minimize the error difference between the adaptive filter output and the desired signal. the mean square error (mse) is the most commonly used metric to quantify this error. adaptive filters are widely used in multiple applications including acoustic noise cancellation, echo cancellation, beam forming, system identification, bio medical signal enhancement, equalization of communication channels, etc. for examples illustrating some of these applications, see , , and .

when the inputs are colored, the affine projection adaptive filter algorithms offered by dsp.affineprojectionfilter object significantly improve the convergence speed over the lms variations. for increased computational cost, adaptive lattice filter algorithm offered by dsp.adaptivelatticefilter object can provide a better convergence over the lms and rls counterparts. you can also implement an adaptive fir filter in the frequency domain using the dsp.frequencydomainadaptivefilter object.

the convergence performance is determined by the trajectory of the actual mse, determined by , and how it converges with the predicted mse, determined by .

objects

compute output, error, and weights using block lms adaptive algorithm
dsp.lmsfiltercompute output, error, and weights of lms adaptive filter
dsp.rlsfiltercompute output, error and coefficients using recursive least squares (rls) algorithm
dsp.affineprojectionfiltercompute output, error and coefficients using affine projection (ap) algorithm
dsp.adaptivelatticefilteradaptive lattice filter
dsp.fasttransversalfilterfast transversal least-squares fir adaptive filter
filtered xlms filter
dsp.frequencydomainadaptivefiltercompute output, error, and coefficients using frequency-domain fir adaptive filter

blocks

compute output, error, and weights using lms adaptive algorithm
compute output, error, and weights using lms adaptive algorithm
compute output, error, and coefficients using frequency domain fir adaptive filter
predict or estimate states of dynamic systems
compute output, error, and weights using lms adaptive algorithm
estimate weights of lms adaptive filter
compute filtered output, filter error, and filter weights for given input and desired signal using rls adaptive filter algorithm

topics


  • general discussion on how adaptive filters work, list of adaptive filter algorithms in dsp system toolbox, convergence performance, and details on few common applications.


  • identify an unknown system using lms algorithm.


  • identify an unknown system using normalized lms algorithm.


  • compare the speed with which the adaptive filter algorithms converge.


  • introduces adaptive filters through a signal enhancement application.


  • perform noise cancellation using sign-data lms algorithm.


  • this example shows how to use a recursive least-squares (rls) filter to identify an unknown system modeled with a lowpass fir filter.


  • perform inverse system identification using dsp.rlsfilter.


  • comparison of rls and lms adaptive filter algorithms.


  • adapt multiple filters independently using the same lms update block. use the adapt port of the lms update block to selectively enable or disable the filters from being adapted.


  • use lms update block as an adaptive linear combiner.


  • design a normalized lms adaptive filter and use it to remove low frequency noise in simulink®.


  • remove colored noise generated from an acoustic environment, using a normalized lms adaptive filter.


  • list of system objects that support variable-sized signals in dsp system toolbox.

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