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

Journal Article · Tue Jan 31 23:00:00 EST 2017 · IEEE Transactions on Signal and Information Processing over Networks

DOI: https://doi.org/10.1109/TSIPN.2017.2662619 · OSTI ID:1394388

Liu, Qiang  ^[1]; Brigham, Katharine ^[2]; Rao, Nageswara S. V. ^[1]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2. Durham Univ. (United Kingdom)

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); Office of Naval Research (ONR) (United States)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1394388
• Journal Information: IEEE Transactions on Signal and Information Processing over Networks, Journal Name: IEEE Transactions on Signal and Information Processing over Networks Journal Issue: 4 Vol. 3; ISSN 2373-7778
• Publisher: IEEECopyright Statement
• Country of Publication: United States
• Language: English