Optimal Remote State Estimation for Self-Propelled Particle Models

TL;DR

This paper develops optimal strategies for remote state estimation of self-propelled particles, balancing estimation accuracy and communication costs, with applications to animal tracking and iterative solution methods.

Contribution

It introduces a framework for designing transmission and estimation policies that optimize a combined cost functional, including novel existence proofs and iterative solution procedures.

Findings

Existence of a jointly optimal solution.

An iterative method to find person-by-person optimal solutions.

Experimental validation demonstrating scheme effectiveness.

Abstract

We investigate the design of a remote state estimation system for a self-propelled particle (SPP). Our framework consists of a sensing unit that accesses the full state of the SPP and an estimator that is remotely located from the sensing unit. The sensing unit must pay a cost when it chooses to transmit information on the state of the SPP to the estimator; and the estimator computes the best estimate of the state of the SPP based on received information. In this paper, we provide methods to design transmission policies and estimation rules for the sensing unit and estimator, respectively, that are optimal for a given cost functional that combines state estimation distortion and communication costs. We consider two notions of optimality: joint optimality and person-by-person optimality. Our main results show the existence of a jointly optimal solution and describe an iterative procedure to find a person-by-person optimal solution. In addition, we explain how the remote estimation scheme can be applied to tracking of animal movements over a costly communication link. We also provide experimental results to show the effectiveness of the scheme.