Probabilistic track coverage in cooperative sensor networks.

The quality of service of a network performing cooperative track detection is represented by the probability of obtaining multiple elementary detections over time along a target track. Recently, two different lines of research, namely, distributed-search theory and geometric transversals, have been used in the literature for deriving the probability of track detection as a function of random and deterministic sensors' positions, respectively. In this paper, we prove that these two approaches are equivalent under the same problem formulation. Also, we present a new performance function that is derived by extending the geometric-transversal approach to the case of random sensors' positions using Poisson flats. As a result, a unified approach for addressing track detection in both deterministic and probabilistic sensor networks is obtained. The new performance function is validated through numerical simulations and is shown to bring about considerable computational savings for both deterministic and probabilistic sensor networks.

Full Text

Duke Authors

Cited Authors

  • Ferrari, S; Zhang, G; Wettergren, TA

Published Date

  • December 2010

Published In

Volume / Issue

  • 40 / 6

Start / End Page

  • 1492 - 1504

PubMed ID

  • 20236903

Electronic International Standard Serial Number (EISSN)

  • 1941-0492

Digital Object Identifier (DOI)

  • 10.1109/TSMCB.2010.2041449

Language

  • eng