Variable Splitting Methods for Constrained State Estimation in Partially Observed Markov Processes

TL;DR

This paper introduces a new class of variable splitting methods for constrained state estimation in partially observed Markov processes, improving efficiency and accuracy over traditional optimization techniques.

Contribution

It develops a generalized variable splitting framework and efficient algorithms that leverage Markovian structure for better constrained state estimation.

Findings

Outperforms conventional optimization in computation cost

Achieves higher estimation accuracy

Demonstrates effectiveness through numerical experiments

Abstract

In this paper, we propose a class of efficient, accurate, and general methods for solving state-estimation problems with equality and inequality constraints. The methods are based on recent developments in variable splitting and partially observed Markov processes. We first present the generalized framework based on variable splitting, then develop efficient methods to solve the state-estimation subproblems arising in the framework. The solutions to these subproblems can be made efficient by leveraging the Markovian structure of the model as is classically done in so-called Bayesian filtering and smoothing methods. The numerical experiments demonstrate that our methods outperform conventional optimization methods in computation cost as well as the estimation performance.