Multistage adaptive stochastic filters

A multistage stochastic adaptive recursive filter is introduced which uses a white noise dither signal at its second stage to avoid the strictly positive real condition existing algorithms used for convergence. In the first stage an autoregressive (AR) model fitted to estimate the first n parameters of the autoregressive portion of the filter. The second stage is used to compute the AR polynomial when the passivity condition is not satisfied. In the third stage, using the models obtained from the first and second stages, an improved autoregressive moving average (ARMA) model is generated. The proposed algorithm is used in two examples: detection and spectral estimation of a narrowband signal corrupted by white noise and identification of a second-order ARMA (autoregressive moving-average) model. Simulation results are compared with results for existing methods