ABSTRACT
This chapter presents a brief review of random signal analysis which are important for the optimal and adaptive processing. Many of the fundamental tools of digital signal processing; the rules of convolution and Fourier transforms, and digital filter designs, are usually first encountered in the framework of deterministic signal analysis. Wold's theorem is a simplified form of a theorem due to Wold which dates back to 1938, and is one of the most fundamental results in random signal analysis. The theorem says simply that any non- deterministic stationary random sequence may be generated by passing white noise through a shift-in variant linear filter. The general Autoregressive Moving Average (ARMA) process is more complicated than either the MA or AR, having a generating model containing both poles and zeros. An ARMA model may obtain the same degree of fidelity with a greatly reduced parameter set compared to either of the simpler models.
