Notions of strong ergodicity for stochastic analysis of multirate systems

For stochastic analysis of single-rate linear systems, a desirable property for stochastic signals is ergodicity in the mean and correlation. Unfortunately, as we show, the ergodicity property may not be preserved under downsampling and uniformly stable linear filtering. This poses a serious problem for stochastic analysis of multirate linear systems. We introduce the notion of strong ergodicity which is preserved under a number of important multirate operations including downsampling, upsampling and time-variant uniformly stable linear filtering. We provide conditions for stochastic processes to be strongly ergodic. Using this result, we show that both independent random processes and bounded deterministic signals are strongly ergodic in the mean and correlation.