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This content will become publicly available on August 1, 2017

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

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.
 [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:
OSTI Identifier:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
IEEE Sensors Journal
Additional Journal Information:
Journal Volume: 16; Journal Issue: 15; Journal ID: ISSN 1530-437X
Research Org:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
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