Mortensen Observer for a class of variational inequalities -Lost equivalence with stochastic filtering approaches
L.P. Chaintron, \'Alvaro Mateos Gonz\'alez, Laurent Mertz, Philippe, Moireau

TL;DR
This paper explores the extension of Mortensen's deterministic estimation method to nonsmooth variational inequality dynamics, revealing a potential breakdown of equivalence with stochastic filtering approaches in non-reversible systems.
Contribution
It introduces a relaxed boundary constraint approach and analyzes the vanishing viscosity limit, linking the Mortensen estimator to control problems rather than estimation in nonsmooth dynamics.
Findings
Proposes an approximated Mortensen estimator for nonsmooth dynamics.
Shows the limiting solution relates to a control problem, not estimation.
Identifies a potential violation of equivalence between deterministic and stochastic methods.
Abstract
We address the problem of deterministic sequential estimation for a nonsmooth dynamics in R governed by a variational inequality, as illustrated by the Skorokhod problem with a reflective boundary condition at 0. For smooth dynamics, Mortensen introduced an energy for the likelihood that the state variable produces-up to perturbations disturbances-a given observation in a finite time interval, while reaching a given target state at the final time. The Mortensen observer is the minimiser of this energy. For dynamics given by a variational inequality and therefore not reversible in time, we study the definition of a Mortensen estimator. On the one hand, we address this problem by relaxing the boundary constraint of the synthetic variable and then proposing an approximated variant of the Mortensen estimator that uses the resulting nonlinear smooth dynamics. On the other hand, inspired by…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
