Adaptive line enhancement using a random AR model

The application of random autoregressive models to signal processing problems (specifically, to adaptive line enhancement) is discussed. The advantage of this approach is that random AR models may reflect more accurately the uncertainty in the stochastic process that generates the received signal. It is shown, through Monte Carlo simulations, that by using random AR models, better results are obtained than by using the conventional deterministic AR models under the same conditions