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A data‐driven particle filter for terrain based navigation of sensor‐limited autonomous underwater vehicles
Author(s) -
Melo José,
Matos Aníbal
Publication year - 2019
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.2107
Subject(s) - robustness (evolution) , terrain , particle filter , computer science , underwater , noise (video) , filter (signal processing) , control theory (sociology) , real time computing , artificial intelligence , computer vision , geography , biochemistry , chemistry , cartography , control (management) , archaeology , image (mathematics) , gene
In this article a new Data‐Driven formulation of the Particle Filter framework is proposed. The new formulation is able to learn an approximate proposal distribution from previous data. By doing so, the need to explicitly model all the disturbances that might affect the system is relaxed. Such characteristics are particularly suited for Terrain Based Navigation for sensor‐limited AUVs, where typical scenarios often include non‐negligible sources of noise affecting the system, which are unknown and hard to model. Numerical results are presented that demonstrate the superior accuracy, robustness and efficiency of the proposed Data‐Driven approach.