MAP moving horizon state estimation with binary measurements
Abstract
The paper addresses state estimation for discrete-time systems with binary (threshold) measurements by following a Maximum A posteriori Probability (MAP) approach and exploiting a Moving Horizon (MH) approximation of the MAP cost-function. It is shown that, for a linear system and noise distributions with log-concave probability density function, the proposed MH-MAP state estimator involves the solution, at each sampling interval, of a convex optimization problem. Application of the MH-MAP estimator to dynamic estimation of a diffusion field given pointwise-in-time-and-space binary measurements of the field is also illustrated and, finally, simulation results relative to this application are shown to demonstrate the effectiveness of the proposed approach.
Cite
@article{arxiv.1804.02167,
title = {MAP moving horizon state estimation with binary measurements},
author = {Giorgio Battistelli and Luigi Chisci and Nicola Forti and Stefano Gherardini},
journal= {arXiv preprint arXiv:1804.02167},
year = {2018}
}