Particle filtering for channel parameter tracking in a noisy shallow ocean environment using a vertical array

Acoustic signals in a shallow ocean environment are severely distorted due to the time-varying and inhomogeneous nature of the propagation channel. In this paper, a state-space model is introduced to characterize the uncertainties of the shallow ocean and a Rao-Blackwellized particle filter (RBPF) is developed to estimate the model parameters. Since both modal functions and horizontal wave numbers of the channel are assumed unknown, the state-space model has a high nonlinearity and high dimensionality. As the modal functions are linear with the measurements conditioning on the horizontal wave numbers, a Kalman filtering (KF) is employed to marginalize out the modal functions. Hence only the horizontal wave numbers need to be estimated by using a PF. Simulation results show that the proposed RBPF algorithm significantly outperforms the existing approaches.