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 work, we pursue learning-based approaches for estimation and fusion of target states in long-haul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). The joint effect of 1) imperfect communication condition, namely, link-level loss and delay, and 2) 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 Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); Office of Naval Research (ONR) (United States)
- Grant/Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1394388
- Alternate ID(s):
- OSTI ID: 1468110
- Journal Information:
- IEEE Transactions on Signal and Information Processing over Networks, Vol. 3, Issue 4; ISSN 2373-7778
- Publisher:
- IEEECopyright Statement
- Country of Publication:
- United States
- Language:
- English
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