An innovations approach to least-squares estimation--Part V: Innovations representations and recursive estimation in colored noise

Abstract
We show that least-squares filtered and smoothed estimates of a random process given observations of another colored noise process can be expressed as certain linear combinations of the state vector of the so-called innovations representation (IR) of the observed process. The IR of a process is a representation of it as the response of a causal and causally invertible linear filter to a white-noise "innovations" process. For nonstationary colored noise processes, the IR may not always exist and a major part of this paper is devoted to the problem of identifying a proper class of such processes and of giving effective recursive algorithms for their determination. The IR can be expressed either in terms of the parameters of a known lumped model for the process or in terms of its covariance function. In the first case, our results on estimation encompass most of those found in the previous literature on the subject; in the second case, there seems to be no prior literature. Finally, we may note that our proofs rely on, and exploit in both directions, the intimate relation that exists between least-squares estimation and the innovations representation.