Adaptive sensor placement and boundary estimation for monitoring mass objects

Document Type

Article

Publication Date

2-1-2008

Abstract

Sensor networks are widely used in monitoring and tracking a large number of objects. Without prior knowledge on the dynamics of object distribution, their density estimation could be learned in an adaptive manner to support effective sensor placement. After sensors observe the "current" locations of objects, the estimates of object distribution are updated with these new observations through a recursive distributed expectation-maximization algorithm. Based on the real-time estimates of object distribution, an adaptive sensor placement algorithm could be designed to achieve stable and high accuracy in tracking mass objects. This paper constructs a Gaussian mixture model to characterize the mixture distribution of object locations and proposes a novel methodology to adaptively update sensor placement. Our simulation results demonstrate the effectiveness of the proposed algorithm for adaptive sensor placement and boundary estimation of mass objects. © 2007 IEEE.

Identifier

39649085339 (Scopus)

Publication Title

IEEE Transactions on Systems Man and Cybernetics Part B Cybernetics

External Full Text Location

https://doi.org/10.1109/TSMCB.2007.910531

ISSN

10834419

PubMed ID

18270093

First Page

222

Last Page

232

Issue

1

Volume

38

Grant

86190NBS21

Fund Ref

U.S. Department of Defense

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