An optimal control approach to particle filtering

TL;DR

This paper introduces a novel particle filtering method for continuous-time systems using an optimal control framework, leveraging path integral control to improve estimation accuracy and fault tolerance over traditional methods.

Contribution

It reformulates particle filtering as an optimal control problem over a finite window, enabling recursive solutions with enhanced robustness and batch-like measurement processing.

Findings

Demonstrates improved fault tolerance in particle filtering.

Shows effectiveness through numerical examples.

Utilizes path integral control for recursive estimation.

Abstract

We present a novel particle filtering framework for continuous-time dynamical systems with continuous-time measurements. Our approach is based on the duality between estimation and optimal control, which allows reformulating the estimation problem over a fixed time window into an optimal control problem. The resulting optimal control problem has a cost function that depends on the measurements and the closed-loop dynamics under optimal control coincides with the posterior distribution over the trajectories for the corresponding estimation problem. This type of stochastic optimal control problem can be solved using a remarkable technique known as path integral control. By recursively solving these optimal control problems using path integral control as new measurements become available we obtain an optimal control-based particle filtering algorithm. A distinguishing feature of the proposed method is that it uses the measurements over a finite-length time window instead of a single measurement for the estimation at each time step, resembling the batch methods of filtering, and improving fault tolerance. The efficacy of our algorithm is illustrated with several numerical examples.