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This paper addresses the problem of distributed fusion when the conditional independence assumptions on sensor measurements or local estimates are not met. A new data fusion algorithm called Copula fusion is presented. The proposed method is grounded on Copula statistical modeling and Bayesian analysis. The primary advantage of the Copula-based methodology is that it could reveal the unknown correlation that allows one to build joint probability distributions with potentially arbitrary underlying marginals and a desired intermodal dependence. The proposed fusion algorithm requires no a priori knowledge of communications patterns or network connectivity. The simulation results show that the Copula fusion brings a consistent estimate for a wide range of process noises. [ABSTRACT FROM AUTHOR]

Underwater Electric Potential is an important signal characteristic of a ship. The signal contains location information which can be used to track the ship. This research tries to study the possibility of ship tracking using Underwater Electric Potential. Aiming at the problems existing in the traditional Kalman filters under large initial errors, a new nonlinear filter is proposed. State space model of ship tracking is established; the problem existing in the ordinary Kalman filters is analyzed from the perspective of Kullbeck-Leibler Divergence; the new algorithm is proposed based on progressive Bayesian; simulations are designed. Simulation results show: it is feasible to use underwater electric potential to track the ship; the new method can effectively improve filter performance under large initial error and can effectively track the ship with preferable precision and convergence, which has great practical value. [ABSTRACT FROM AUTHOR]