System Identification using LMS Adaptive Filter

1. What is LMS adaptive filter?
2. What is adaptive system identification?
3. What is the use of adaptive filter?
4. How does LMS algorithm work?

What is LMS adaptive filter?

Least mean squares (LMS) algorithms are a class of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean square of the error signal (difference between the desired and the actual signal).

What is adaptive system identification?

System identification is the process of identifying the coefficients of an unknown system using an adaptive filter. The general overview of the process is shown in System Identification –– Using an Adaptive Filter to Identify an Unknown System. The main components involved are: The adaptive filter algorithm.

What is the use of adaptive filter?

One common adaptive filter application is to use adaptive filters to identify an unknown system, such as the response of an unknown communications channel or the frequency response of an auditorium, to pick fairly divergent applications. Other applications include echo cancellation and channel identification.

How does LMS algorithm work?

LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which eventually leads to the minimum mean square error.