Always choose second best: Tracking a moving target on a graph with a noisy binary sensor

Kevin Leahy, Mac Schwager

2016 European Control Conference (ECC), 2016

Abstract

In this work, we consider the problem of using a noisy binary sensor to optimally track a target that moves as a Markov Chain in a ﬁnite discrete environment. Our approach focuses on one-step optimality because of the apparent infeasibility of computing an optimal policy via dynamic programming. We prove that always searching in the second most likely location minimizes one-step variance while maximizing the belief about the target’s location over one step. Simulation results demonstrate the performance of our strategy and suggest the policy performs well over arbitrary horizons.

BibTeX

@inproceedings{leahy_always_2016,
  address = {Aalborg, Denmark},
  title = {Always choose second best: {Tracking} a moving target on a graph with a noisy binary sensor},
  isbn = {978-1-5090-2591-6},
  shorttitle = {Always choose second best},
  url = {http://ieeexplore.ieee.org/document/7810538/},
  abstract = {In this work, we consider the problem of using a noisy binary sensor to optimally track a target that moves as a Markov Chain in a ﬁnite discrete environment. Our approach focuses on one-step optimality because of the apparent infeasibility of computing an optimal policy via dynamic programming. We prove that always searching in the second most likely location minimizes one-step variance while maximizing the belief about the target’s location over one step. Simulation results demonstrate the performance of our strategy and suggest the policy performs well over arbitrary horizons.},
  language = {en},
  urldate = {2020-09-15},
  booktitle = {2016 {European} {Control} {Conference} ({ECC})},
  publisher = {IEEE},
  author = {Leahy, Kevin and Schwager, Mac},
  month = jun,
  year = {2016},
  keywords = {state estimation, planning},
  pages = {1715--1721},
  month_numeric = {6}
}