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Title: Staggered scheduling of sensor estimation and fusion for tracking over long-haul links

Abstract

Networked sensing can be found in a multitude of real-world applications. Here, we focus on the communication-and computation-constrained long-haul sensor networks, where sensors are remotely deployed over a vast geographical area to perform certain tasks. Of special interest is a class of such networks where sensors take measurements of one or more dynamic targets and send their state estimates to a remote fusion center via long-haul satellite links. The severe loss and delay over such links can easily reduce the amount of sensor data received by the fusion center, thereby limiting the potential information fusion gain and resulting in suboptimal tracking performance. In this paper, starting with the temporal-domain staggered estimation for an individual sensor, we explore the impact of the so-called intra-state prediction and retrodiction on estimation errors. We then investigate the effect of such estimation scheduling across different sensors on the spatial-domain fusion performance, where the sensing time epochs across sensors are scheduled in an asynchronous and staggered manner. In particular, the impact of communication delay and loss as well as sensor bias on such scheduling is explored by means of numerical and simulation studies that demonstrate the validity of our analysis.

Authors:
 [1];  [1];  [2]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. State Univ. of New York at Stony Brook, Stony Brook, NY (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1327735
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Sensors Journal
Additional Journal Information:
Journal Volume: 16; Journal Issue: 15; Journal ID: ISSN 1530-437X
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; reporting latency; long-haul sensor networks; state estimate fusion; asynchronous and staggered estimation; intra-state and inter-state prediction and retrodiction; mean-square-error (MSE) and root-mean-square-error (RMSE) performance; sensor fusion; estimation; target tracking; extraterrestrial measurements; delays; scheduling

Citation Formats

Liu, Qiang, Rao, Nageswara S. V., and Wang, Xin. Staggered scheduling of sensor estimation and fusion for tracking over long-haul links. United States: N. p., 2016. Web. doi:10.1109/JSEN.2016.2575099.
Liu, Qiang, Rao, Nageswara S. V., & Wang, Xin. Staggered scheduling of sensor estimation and fusion for tracking over long-haul links. United States. doi:10.1109/JSEN.2016.2575099.
Liu, Qiang, Rao, Nageswara S. V., and Wang, Xin. Mon . "Staggered scheduling of sensor estimation and fusion for tracking over long-haul links". United States. doi:10.1109/JSEN.2016.2575099. https://www.osti.gov/servlets/purl/1327735.
@article{osti_1327735,
title = {Staggered scheduling of sensor estimation and fusion for tracking over long-haul links},
author = {Liu, Qiang and Rao, Nageswara S. V. and Wang, Xin},
abstractNote = {Networked sensing can be found in a multitude of real-world applications. Here, we focus on the communication-and computation-constrained long-haul sensor networks, where sensors are remotely deployed over a vast geographical area to perform certain tasks. Of special interest is a class of such networks where sensors take measurements of one or more dynamic targets and send their state estimates to a remote fusion center via long-haul satellite links. The severe loss and delay over such links can easily reduce the amount of sensor data received by the fusion center, thereby limiting the potential information fusion gain and resulting in suboptimal tracking performance. In this paper, starting with the temporal-domain staggered estimation for an individual sensor, we explore the impact of the so-called intra-state prediction and retrodiction on estimation errors. We then investigate the effect of such estimation scheduling across different sensors on the spatial-domain fusion performance, where the sensing time epochs across sensors are scheduled in an asynchronous and staggered manner. In particular, the impact of communication delay and loss as well as sensor bias on such scheduling is explored by means of numerical and simulation studies that demonstrate the validity of our analysis.},
doi = {10.1109/JSEN.2016.2575099},
journal = {IEEE Sensors Journal},
number = 15,
volume = 16,
place = {United States},
year = {Mon Aug 01 00:00:00 EDT 2016},
month = {Mon Aug 01 00:00:00 EDT 2016}
}

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