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Title: Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics – such as the position – is expected to possess much improved quality. In this paper, we pursue learning-based approaches for estimation and fusion of target states in longhaul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). Finally, the joint effect of (i) imperfect communication condition, namely, link-level loss and delay, and (ii) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.
Authors:
ORCiD logo [1] ;  [2] ;  [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Durham Univ. (United Kingdom)
Publication Date:
Grant/Contract Number:
AC05-00OR22725
Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Signal and Information Processing over Networks
Additional Journal Information:
Journal Volume: 3; Journal Issue: 4; Journal ID: ISSN 2373-7778
Publisher:
IEEE
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); Office of Naval Research (ONR) (United States)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; estimation; information processing; fuses; testing; artificial neural networks; target tracking; reporting deadline; long-haul sensor networks; state estimate fusion; error regularization; root-mean-square-error (RMSE) performance
OSTI Identifier:
1394388