Sequential inversion of highly nonlinear time-evolving sound speed profiles

Affected by ocean dynamic processes such as sea waves,rainfall and internal waves,the time evolution of sound speed profiles(SSPs) in shallow water is highly nonlinear.To solve the problem,an algorithm of the improved particle filter is implemented for the tracking of time-evolving SSPs.Based on the Empirical Orthogonal Functions(EOFs) and the state-space model which describe the evolution characteristics of SSPs,sequential inversion of SSPs are carried out through the acoustic pressure data received by the Vertical Line Array(VLA) using UPF.Time-evolving SSPs are estimated via the acoustic array data simulated by the measured SSPs and prior seabed acoustic properties.The algorithm was validated and result shows that under the comparable computational efficiency,the UPF-based method can overcome the divergences of Ensemble Kalman Filter(EnKF) algorithm and keeps up perfect tracking performance at the jump time.The estimated accuracy can be effectively increased,especially in the case of strongly time-evolving SSPs.