Improved event-based particle filtering in resource-constrained remote state estimation

TL;DR

This paper addresses practical challenges in applying particle filters to resource-efficient event-based remote state estimation, proposing solutions to sample degeneracy, communication issues, and computational load.

Contribution

It introduces a new approach using precomputed state estimates and discusses solutions to sample degeneracy and computational challenges in event-based particle filtering.

Findings

Auxiliary particle filter reduces sample degeneracy risk.

Precomputed state estimates improve observer-to-sensor communication.

Discussion on computational load highlights implementation challenges.

Abstract

Event-based sampling has been proposed as a general technique for lowering the average communication rate in remote state estimation, which can be important in scenarios with constraints on resources such as network bandwidth or sensor energy. Recently, the interest of applying particle filters to event-based state estimation has seen an upswing, partly to tackle nonlinear and non-Gaussian problems, but also since event-based sampling does not allow an analytic solution for linear--Gaussian systems. Thus far, very little has been mentioned regarding the practical issues that arise when applying particle filtering to event-based state estimation. In this paper, we provide such a discussion by (i) demonstrating that there exists a high risk of sample degeneracy at new events, for which the auxiliary particle filter provides an intuitive solution, (ii) introducing a new alternative to the local predictor approach based on precomputing state estimates which is better suited to solve the issue of observer-to-sensor communication for closed-loop triggering in difficult systems, and (iii) exploring the difficulties surrounding the increased computational load when implementing the particle filter under event-based sampling.