Recursive blind minimax estimation: improving mse over recursive least squares

We consider the problem of on-line (or recursive) parameter estimation in which, at each moment, an unknown deterministic parameter vector must be re-estimated from measurements corrupted by additive noise. We present efficient algorithms for calculating two variants of the blind minimax estimator, which is a biased estimator proven to outperform least squares in terms of mean squared error. These operate in the same setting as the recursive least squares (RLS) method and utilize it. Both algorithms have a computational complexity in par with RLS. We discuss the advantages and shortcomings of the presented methods and demonstrate through simulations situations in which they produce substantial gain over RLS.