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 System Identification of FIR Filter Using LMS Algorithm, Noise Cancellation Using Sign-Data LMS Algorithm, and Inverse System Identification Using RLS Algorithm.
When the inputs are colored, the affine projection adaptive filter
algorithms offered by
object significantly improve the convergence speed over the LMS
variations. For increased computational cost, adaptive lattice filter
algorithm offered by
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
|Compute output, error, and weights using block LMS adaptive algorithm|
|Compute output, error, and weights of LMS adaptive filter|
|Compute output, error and coefficients using recursive least squares (RLS) algorithm|
|Compute output, error and coefficients using affine projection (AP) Algorithm|
|Adaptive lattice filter|
|Fast transversal least-squares FIR adaptive filter|
|Filtered XLMS filter|
|Compute output, error, and coefficients using frequency-domain FIR adaptive filter|
|Estimate system measurements and states using Kalman filter|
|Block LMS Filter||Compute output, error, and weights using LMS adaptive algorithm|
|Fast Block LMS Filter||Compute output, error, and weights using LMS adaptive algorithm|
|Frequency-Domain Adaptive Filter||Compute output, error, and coefficients using frequency domain FIR adaptive filter|
|Kalman Filter||Predict or estimate states of dynamic systems|
|LMS Filter||Compute output, error, and weights using LMS adaptive algorithm|
|LMS Update||Estimate weights of LMS adaptive filter|
|RLS Filter||Compute filtered output, filter error, and filter weights for given input and desired signal using RLS adaptive filter algorithm|
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.
Comparison of RLS and LMS adaptive filter algorithms.
Perform inverse system identification using dsp.RLSFilter.
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 which support variable-sized signals in DSP System Toolbox.