UCSD

Mechanical and Aerospace Engineering

• Home
• Publications
• Group
• Teaching
• Software
• Lab

---

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