Singularity in CM Sequences

TL;DR

This paper develops models and characterizations for both singular and nonsingular Gaussian conditionally Markov sequences, including reciprocal and Markov processes, broadening their applicability beyond previous nonsingular assumptions.

Contribution

It introduces the first dynamic models for general Gaussian reciprocal sequences and unifies the treatment of singular and nonsingular CM sequences from a CM perspective.

Findings

Unified framework for singular and nonsingular Gaussian CM sequences

New dynamic models for Gaussian reciprocal sequences

Application examples in trajectory modeling

Abstract

Most existing results about modeling and characterizing Gaussian Markov, reciprocal, and conditionally Markov (CM) processes assume nonsingularity of the processes. This assumption makes the analysis easier, but restricts application of these processes. This paper studies, models, and characterizes the general (singular/nonsingular) Gaussian CM (including reciprocal and Markov) sequence. For example, to our knowledge, there is no dynamic model for the general (singular/nonsingular) Gaussian reciprocal sequence in the literature. We obtain two such models from the CM viewpoint. As a result, the significance of studying reciprocal sequences from the CM viewpoint is demonstrated. The results of this paper unify singular and nonsingular Gaussian CM (including reciprocal and Markov) sequences and provide tools for their application. An application of CM sequences in trajectory modeling with a destination is discussed and illustrative examples are presented.