### Jorge Cortés

#### Professor

Estimation-based ocean flow field reconstruction using profiling floats

H. Fang, R. A. de Callafon, J. Cortés

Offshore Mechatronics Systems Engineering, ed. H. R. Karim, CRC Press, 2018, pp. 40-65

### Abstract

This note considers ocean flow field monitoring using profiling
floats and investigates a foundational estimation problem underlying
flow field reconstruction, which is known as simultaneous input and
state estimation. We take a Bayesian perspective to develop the
needed estimation approaches. With this perspective, we first build
Bayesian estimation principles for input and state estimation in
both the cases of filtering and smoothing. Then, we formulate
Maximum a Posteriori estimation problems and solve them using the
classical Gauss-Newton method, leading to a set of algorithms to
accomplish input and state estimation. The proposed algorithms
represent a development of the Bayesian estimation theory and
generalize a number of relevant methods in the literature. We
illustrate the effectiveness of our approach in addressing an
oceanographic flow field estimation problem based on profiling
floats that measure position intermittently and acceleration
continuously.

pdf

Mechanical and Aerospace Engineering,
University of California, San Diego

9500 Gilman Dr,
La Jolla, California, 92093-0411

Ph: 1-858-822-7930

Fax: 1-858-822-3107

cortes at ucsd.edu

Skype id:
jorgilliyo