State-Space Realizations and Optimal Smoothing for Gaussian Generalized Reciprocal Processes

Abstract

This technical note derives stochastic realization and optimal smoothing algorithms for a class of Gaussian generalized reciprocal processes (GGRP). The note exploits the interplay between reciprocal processes and Markov bridges which underpin the GGRP model. A forward–backward algorithm for stochastic realization of GGRP is described. The form on the inverse covariance matrix for the GGRP is used, via Cholesky factorization, to derive a procedure for optimal (MMSE) smoothing of GGRP observed in noise. The note demonstrates that the associated smoothing error is also a GGRP with known covariance which may be used to assess the performance of smoothing as a function of the model parameters. A numerical example is provided to illustrate the performance of the MMSE smoother compared to those derived from compatible Markov and reciprocal model-based algorithms.