By Topic

D-MAP: Distributed Maximum a Posteriori Probability Estimation of Dynamic Systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Felicia Y. Jakubiec ; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia ; Alejandro Ribeiro

This paper develops a framework for the estimation of a time-varying random signal using a distributed sensor network. Given a continuous time model sensors collect noisy observations and produce local estimates according to the discrete time equivalent system defined by the sampling period of observations. Estimation is performed using a maximum a posteriori probability estimator (MAP) within a given window of interest. To mediate the incorporation of information from other sensors we introduce Lagrange multipliers to penalize the disagreement between neighboring estimates. We show that the resulting distributed (D)-MAP algorithm is able to track dynamical signals with a small error. This error is characterized in terms of problem constants and vanishes with the sampling time as long as the log-likelihood function which is assumed to be log-concave satisfies a smoothness condition. We implement the D-MAP algorithm for a linear and a nonlinear system model to show that the performance corroborates with theoretical findings.

Published in:

IEEE Transactions on Signal Processing  (Volume:61 ,  Issue: 2 )