Recursive estimation of signals in an autoregressive noise

Most methods estimating noisy sinusoidal signals assume the noise to be white, and fail when they are used on real signals with colored noise. We propose two new recursive algorithms, deduced from the work of Kay and Nagesha (see IEEE Trans. on Signal Proc., vol.42, no.1, p.88, 1994), for the estimation of sinusoidal signals embedded in an AR noise. The first one is a λ-RLS, whereas the second one uses Kalman filtering. Their convergence speed, computational burden and statistical characteristics are compared and the advantages brought by these estimators for real signals are shown