Underwater object detection based on geophysical inversion information

A geophysical inversion information based underwater object detection method is proposed by using the joint Gravity-Gradient and Magnetic-Gradient Inversion algorithms. The gravity-gradient and magnetic-gradient inversion equations are combined to estimate the orientation and distance of the underwater object. After calculating the relative positions of underwater object from the gravity-gradient inversion equations and magnetic-gradient inversion equations, the BP Neural Network is exploited to obtain an optimal geophysical inversion equation applied to underwater object detection. A typical three layered neural network of 6 input and 3 output neurons with a single hidden layer is constructed to realize information fusion. The leading characteristics of such neural network are strong parallel computing, learning and adaptive capabilities, as well as good fault-tolerance. With the proposed method, the trajectories of an underwater object can be detected accurately. Simulation results show that our method is more efficient than the joint gravity-gradient and magnetic-gradient inversion methods.