Adaptive line enhancement via subspace tracking

We propose a new algorithm for adaptive line enhancement. The objective is to improve the signal-to-noise ratio of narrowband signals in additive white noise by adaptive filtering. By recursively updating an estimate of a rank I signal subspace we develop a fast algorithm having the same computational complexity as LMS i.e. O(m), where m is the filter length. We demonstrate the superiority of this approach over LMS in terms of the rate of convergence and the ability to track LFM signals.