Distributed localization in wireless sensor networks as a pre-image problem in a reproducing kernel Hilbert space
Résumé
In this paper, we introduce a distributed strategy for localization in a wireless sensor network composed of limited range sensors. The proposed distributed algorithm provides sensor position estimation from local similarity measurements. Incremental Kernel Principal Component Analysis techniques are used to build the nonlinear manifold linking anchor nodes. Non-anchor nodes positions are estimated by the pre-image of their nonlinear projection onto this manifold. This non-linear strategy provides a great accuracy when data of interest are highly corrupted by noise and when sensors are not able to estimate their Euclidean inter-distances.
Mots clés
machine learning
sparsity
adaptive filtering
estimation theory
Hilbert spaces
sensor placement
distributed localization
preimage problem
kernel Hilbert space
limited range sensors
sensor position estimation
local similarity measurements
incremental kernel principal component analysis technique
nonlinear manifold linking anchor nodes
nonanchor node position estimation
Euclidean inter-distances
Sensors
Kernel
Signal processing algorithms
Wireless sensor networks
Principal component analysis
Vectors
Signal processing
| Origine | Fichiers produits par l'(les) auteur(s) |
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