DocumentCode :
3656895
Title :
Artificial neural networks for estimation and fusion in long-haul sensor networks
Author :
Qiang Liu;Xin Wang;Nageswara S. V. Rao
Author_Institution :
Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794-2350
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
460
Lastpage :
467
Abstract :
We consider long-haul sensor networks where 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 to improve the accuracy of the final estimates of certain target characteristics. In this work, we pursue artificial neural network (ANN) learning-based approaches for estimation and fusion of target states in long-haul sensor networks. 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.
Keywords :
"Training","Estimation","Testing","Target tracking","Artificial neural networks","Delays"
Publisher :
ieee
Conference_Titel :
Information Fusion (Fusion), 2015 18th International Conference on
Type :
conf
Filename :
7266597
Link To Document :
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