Distributed Recursive Gaussian Processes for RSS Map Applied to Target Tracking

We propose a distributed recursive Gaussian process (drGP) regression framework for building received-signal-strength (RSS) map. The proposed framework adopts independent mobile devices in prescribed local areas to construct local RSS maps through recursive computation of the posterior distribution...

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Veröffentlicht in:IEEE journal of selected topics in signal processing Jg. 11; H. 3; S. 492 - 503
Hauptverfasser: Feng Yin, Gunnarsson, Fredrik
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Approximation algorithms
Big Data
Complexity
Distributed algorithm
Fingerprinting
Gaussian process
Gaussian processes
Kernel
Mobile communication
particle filtering
recursive algorithm
Recursive functions
RSS fingerprinting
Signal processing algorithms
Tracking
Training
ISSN:1932-4553, 1941-0484
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Zusammenfassung:We propose a distributed recursive Gaussian process (drGP) regression framework for building received-signal-strength (RSS) map. The proposed framework adopts independent mobile devices in prescribed local areas to construct local RSS maps through recursive computation of the posterior distribution of the RSS on a fixed set of grids as training data gradually become available. The training input positions can be either precise or subject to errors of known distribution. All the local RSS maps are then fused to give a global map in the second step. The proposed framework is of significantly reduced computational complexity and scalable to big data generated from large-scale sensor networks. We further demonstrate its use in both static fingerprinting and mobile target tracking. The experimental results show that with our distributed framework satisfactory positioning accuracy can be achieved with much less complexity and storage than the standard framework.
Bibliographie:ObjectType-Article-1
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ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2017.2678105