Optimal Remote State Estimation for Self-Propelled Particle Models
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.
Keywords
Cite
@article{arxiv.1603.04964,
title = {Optimal Remote State Estimation for Self-Propelled Particle Models},
author = {Shinkyu Park and Nuno C. Martins},
journal= {arXiv preprint arXiv:1603.04964},
year = {2016}
}
Comments
a part of the article was submitted to IEEE Conference on Decision and Control 2016