New results in Sridhar filtering theory: The discrete case

A nonlinear time-varying adaptive filter is introduced, and its derivation using optimal control concepts is given in detail. The filter, which is called the discrete Pontryagin filter, is basically an extension to Sridhar filtering theory. The proposed approach can easily replace the conventional methods of autoregressive (AR) and autoregressive moving average (ARMA) models in their many applications. Instead of using a large number of time-invariant parameters to describe the signal or the time series, a single time-varying function is enough. This function is estimated using optimization techniques. Many features are gained using this approach, such as simpler and compact filter equations and better overall accuracy. The statistical properties of the filter are given, and it is shown that the signal estimate will converge in thepth mean to the true value.