Physics-based coastal current tomographic tracking using a Kalman filter

Abstract

Ocean acoustic tomography can be used based on measurements of two-way travel-time differences between the nodes deployed on the perimeter of the surveying area to invert/map the ocean current inside the area. Data at different times can be related using a Kalman filter, and given an ocean circulation model, one can in principle now cast and even forecast current distribution given an initial distribution and/or the travel-time difference data on the boundary. However, an ocean circulation model requires many inputs (many of them often not available) and is unpractical for estimation of the current field. A simplified form of the discretized Navier-Stokes equation is used to show that the future velocity state is just a weighted spatial average of the current state. These weights could be obtained from an ocean circulation model, but here in a data driven approach, auto-regressive methods are used to obtain the time and space dependent weights from the data. It is shown, based on simulated data, that the current field tracked using a Kalman filter (with an arbitrary initial condition) is more accurate than that estimated by the standard methods where data at different times are treated independently. Real data are also examined.

Reconstruction of horizontal tidal current fields in a shallow water with model-oriented coastal acoustic tomography

“…Because of its usefulness, the concept of OAT and reciprocal acoustic transmission have further been applied to a very small port (e.g., Ogasawara and Mori, 2016) and/or rivers (e.g., Kawanisi et al, 2017). Additionally, multiple new approaches have been proposed, such as deploying networked array (Huang et al, 2013;Chen et al, 2021), moving ship tomography (Huang et al, 2019) originating from the work by Cornuelle et al (1989), or physics based approach (Wang et al, 2018).…”