Spatiotemporal Tracking of Ocean Current Field With Distributed Acoustic Sensor Network

Abstract

A distributed networked underwater sensor (DNUS) system can provide ocean measurements over a wide area with a large number of sensors. This paper studies the estimation of the ocean current field observed by a DNUS system. Considering that the current field is correlated in time and space, we present a statistically-based acoustic travel time difference tomography method based on a Kalman filter (KF) to reconstruct and track the ocean current field. A spatiotemporal autoregressive (AR) model is used to describe the time evolution of the current field. In the spatiotemporal AR model, the observation region is divided into subtriangle grids. The subtriangles are partitioned into clusters and each cluster is assigned with one AR coefficient. Moreover, the AR coefficients are updated adaptively with the past estimated current velocities. The proposed method is verified with the synthetic observational data generated by a barotropic ocean model. Compared with the regular distributed processing method, the proposed ocean current field reconstruction and tracking method achieves a lower region-integrated root-mean-square error (RMSE). In addition, by making use of the spatiotemporal correlation, the proposed method is robust to the measurement link failure and burst errors in the DNUS system.