Recursive least squares with forgetting factor

1. What is the forgetting factor?
2. What is recursive least square method?
3. What is the purpose of the recursive least squares estimation?

What is the forgetting factor?

Abstract: The overall performance of the recursive least-squares (RLS) algorithm is governed by the forgetting factor. The value of this parameter leads to a compromise between low misadjustment and stability on the one hand, and fast convergence rate and tracking on the other hand.

What is recursive least square method?

Recursive least squares (RLS) is an adaptive filter algorithm that recursively finds the coefficients that minimize a weighted linear least squares cost function relating to the input signals. This approach is in contrast to other algorithms such as the least mean squares (LMS) that aim to reduce the mean square error.

What is the purpose of the recursive least squares estimation?

The Recursive Least Squares Estimator estimates the parameters of a system using a model that is linear in those parameters. Such a system has the following form: y ( t ) = H ( t ) θ ( t ) . y and H are known quantities that you provide to the block to estimate θ.