Artificial neural networks for estimation and fusion in long-haul sensor networks

Qiang Liu; Xin Wang; Nageswara S.V. Rao

Artificial neural networks for estimation and fusion in long-haul sensor networks

Qiang Liu
, Xin Wang
, Nageswara S.V. Rao

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

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.

Original language	English
Title of host publication	2015 18th International Conference on Information Fusion, Fusion 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	460-467
Number of pages	8
ISBN (Electronic)	9780982443866
State	Published - Sep 14 2015
Event	18th International Conference on Information Fusion, Fusion 2015 - Washington, United States Duration: Jul 6 2015 → Jul 9 2015

Publication series

Name	2015 18th International Conference on Information Fusion, Fusion 2015

Conference

Conference	18th International Conference on Information Fusion, Fusion 2015
Country/Territory	United States
City	Washington
Period	07/6/15 → 07/9/15

Keywords

Long-haul sensor networks
artificial neural networks
backpropagation
error regularization
estimation bias
reporting deadline
root-mean-square-error (RMSE) performance
state estimate fusion

Cite this

@inproceedings{b058b0b470434db1ba8be7affe9ec44a,

title = "Artificial neural networks for estimation and fusion in long-haul sensor networks",

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 = "Long-haul sensor networks, artificial neural networks, backpropagation, error regularization, estimation bias, reporting deadline, root-mean-square-error (RMSE) performance, state estimate fusion",

author = "Qiang Liu and Xin Wang and Rao, \{Nageswara S.V.\}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 18th International Conference on Information Fusion, Fusion 2015 ; Conference date: 06-07-2015 Through 09-07-2015",

year = "2015",

month = sep,

day = "14",

language = "English",

series = "2015 18th International Conference on Information Fusion, Fusion 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "460--467",

booktitle = "2015 18th International Conference on Information Fusion, Fusion 2015",

}

Liu, Q, Wang, X & Rao, NSV 2015, Artificial neural networks for estimation and fusion in long-haul sensor networks. in 2015 18th International Conference on Information Fusion, Fusion 2015., 7266597, 2015 18th International Conference on Information Fusion, Fusion 2015, Institute of Electrical and Electronics Engineers Inc., pp. 460-467, 18th International Conference on Information Fusion, Fusion 2015, Washington, United States, 07/6/15.

Artificial neural networks for estimation and fusion in long-haul sensor networks. / Liu, Qiang; Wang, Xin; Rao, Nageswara S.V.
2015 18th International Conference on Information Fusion, Fusion 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 460-467 7266597 (2015 18th International Conference on Information Fusion, Fusion 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Artificial neural networks for estimation and fusion in long-haul sensor networks

AU - Liu, Qiang

AU - Wang, Xin

AU - Rao, Nageswara S.V.

PY - 2015/9/14

Y1 - 2015/9/14

N2 - 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.

AB - 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.

KW - Long-haul sensor networks

KW - artificial neural networks

KW - backpropagation

KW - error regularization

KW - estimation bias

KW - reporting deadline

KW - root-mean-square-error (RMSE) performance

KW - state estimate fusion

UR - https://www.scopus.com/pages/publications/84960500846

M3 - Conference contribution

AN - SCOPUS:84960500846

T3 - 2015 18th International Conference on Information Fusion, Fusion 2015

SP - 460

EP - 467

BT - 2015 18th International Conference on Information Fusion, Fusion 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 18th International Conference on Information Fusion, Fusion 2015

Y2 - 6 July 2015 through 9 July 2015

ER -

Artificial neural networks for estimation and fusion in long-haul sensor networks

Abstract

Publication series

Conference

Keywords

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