Abstract
Principal Component Analysis has been recently proposed as a nonlinear positioning sensor in the development of tools for Terrain Based Navigation of Underwater Vehicles [10]. In this work the error sources affecting the proposed unsupervised methodology will be enumerated, the stochastic characterization will be studied, and the attainable performance will be discussed. Based on a series of Monte Carlo experiments for a large set of synthesized terrains, conclusions will be drawn on the adequacy of the proposed nonlinear approach.
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Oliveira, P. (2005). PCA Positioning Sensor Characterization for Terrain Based Navigation of UVs. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3522. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492429_74
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DOI: https://doi.org/10.1007/11492429_74
Publisher Name: Springer, Berlin, Heidelberg
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