Sensor Placement for Flapping Wing Model Using Stochastic Observability Gramians

TL;DR

This paper introduces a stochastic framework for sensor placement that leverages process noise to enhance observability analysis and filter performance in nonlinear, bioinspired systems.

Contribution

It develops a novel stochastic observability Gramian approach for sensor placement, contrasting traditional deterministic methods and demonstrating its effectiveness through numerical experiments.

Findings

Stochastic observability can be improved by strategic sensor placement.

Process noise can reveal system states otherwise unobservable.

Numerical results show enhanced filter accuracy with the proposed method.

Abstract

Systems in nature are stochastic as well as nonlinear. In traditional applications, engineered filters aim to minimize the stochastic effects caused by process and measurement noise. Conversely, a previous study showed that the process noise can reveal the observability of a system that was initially categorized as unobservable when deterministic tools were used. In this paper, we develop a stochastic framework to explore observability analysis and sensor placement. This framework allows for direct studies of the effects of stochasticity on optimal sensor placement and selection to improve filter error covariance. Numerical results are presented for sensor selection that optimizes stochastic empirical observability in a bioinspired setting.