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

Journal Article · Mon Aug 01 00:00:00 EDT 2016 · IEEE Sensors Journal

DOI:https://doi.org/10.1109/JSEN.2016.2575099· OSTI ID:1327735

Liu, Qiang ^[1]; Rao, Nageswara S. V. ^[1]; Wang, Xin ^[2]

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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)

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.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1327735
• Journal Information: IEEE Sensors Journal, Vol. 16, Issue 15; ISSN 1530-437X
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

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