## Abstract

The prediction of water current (WC) is key in understanding oceanographic phenomena, and serves as a corner stone for maritime applications. While numerical models are capable to characterize the main phenomenon effecting the current, a good estimate of the current's velocity requires accurate environmental information such as topographic maps, temperature, and wind velocity. Without such information, common practice involves deploying a large number of drifting devices to measure the WC in good resolution. In this paper, we purpose a statistical approach to predict the WC's velocity field, namely, the relation between the drifters' locations and their drifting velocity to estimate the velocity of the WC across a given environment. We offer two statistical approaches. Assuming the above relation is linear, the first uses a weighted least squares to estimate the parameters of the velocity field. The second uses a support vector regression to predict the WC by a classifier. The first approach is more traceable, while the second can manage a non-linear space-speed relation of the WC. Results are studied numerically using an WC model, and experimentally from a sea trial performed across the shores of San Diego including 13 acoustically localized submerged drifters. In both cases, performances show a good agreement between the ground truth of the WC and the predicted one.

Original language | English |
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Article number | 9097852 |

Pages (from-to) | 11598-11607 |

Number of pages | 10 |

Journal | IEEE Sensors Journal |

Volume | 20 |

Issue number | 19 |

DOIs | |

State | Published - 1 Oct 2020 |

### Bibliographical note

Publisher Copyright:© 2001-2012 IEEE.

## Keywords

- Water current
- acoustic localization
- drifter
- least squares
- support vector regression
- water current velocity field

## ASJC Scopus subject areas

- Instrumentation
- Electrical and Electronic Engineering