Localisation and tracking of underwater acoustic source using a modified particle filter

This paper is concerned with the problem of recursively estimating the kinematics of a target moving in underwater environments. The measurements are noise-corrupted time differences of arrival (TDOA) of the signal emitted from the source to pairs of spatially separated sensors. The errors associated with these measurements are very small so standard nonlinear filtering techniques, such as particle filter, can not be directly applied. The reason is that the likelihood function may concentrate in a region of the state space that is too small to contain samples of the particle system. We apply the improved approximation based on progressive correction principle to efficiently handle this difficult problem and describe a new approach to finding an optimal sequence of fictitious matrices to be used in the correction substeps. The performance of the proposed filter is tested using Monte Carlo simulations and the resulting mean-square errors in the positition and velocity are compared to their theoretical Cramer-Rao lower bounds.