Exact finite-dimensional filters for doubly stochastic auto-regressive processes
Vikram Krishnamurthy, Robert James Elliott · IEEE Transactions on Automatic Control · 1997
In this paper, we derive exact finite-dimensional recursive filters for a class of doubly stochastic auto-regressive (AR) models. We assume that the parameters of the doubly stochastic AR process vary according to a nonlinear polynomial function of a Gaussian state-space process. Apart from being of mathematical interest, these finite-dimensional filters have potential applications in time-series analysis and image-enhanced tracking of maneuvering targets.