Distributed localization in wireless sensor networks as a pre-image problem in a reproducing kernel Hilbert space

Mehdi Essoloh (1) , Cédric Richard (2) , Hichem Snoussi (1) , Paul Honeine (1)

1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
2 STMR - Sciences et Technologies pour la Maitrise des Risques

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

Domaines: Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Mehdi Essoloh, Cédric Richard, Hichem Snoussi, Paul Honeine. Distributed localization in wireless sensor networks as a pre-image problem in a reproducing kernel Hilbert space. Proc. 16th European Conference on Signal Processing (EUSIPCO), 2008, Lausanne, Switzerland. pp.1-5. ⟨hal-01966055⟩