Swarm Underwater Acoustic 3D Localization: Kalman vs Monte Carlo | Zendy

Sergio Taraglio | Zendy; Fabio Fratichini | Zendy

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Swarm Underwater Acoustic 3D Localization: Kalman vs Monte Carlo

Author(s) -

Sergio Taraglio,

Fabio Fratichini

Publication year - 2015

Publication title -

international journal of advanced robotic systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.394

H-Index - 46

eISSN - 1729-8814

pISSN - 1729-8806

DOI - 10.5772/61034

Subject(s) - computer science , monte carlo method , extended kalman filter , robustness (evolution) , particle filter , kalman filter , sonar , underwater , algorithm , computer vision , artificial intelligence , acoustics , mathematics , physics , biochemistry , statistics , chemistry , oceanography , gene , geology

Two three-dimensional localization algorithms for a swarm of underwater vehicles are presented. The first is grounded on an extended Kalman filter (EKF) scheme used to fuse some proprioceptive data such as the vessel's speed and some exteroceptive measurements such as the time of flight (TOF) sonar distance of the companion vessels. The second is a Monte Carlo particle filter localization processing the same sensory data suite. The results of several simulations using the two approaches are presented, with comparison. The case of a supporting surface vessel is also considered. An analysis of the robustness of the two approaches against some system parameters is given

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