ML Source Localization Theory in an Underwater Wireless Sensor Array Network

Abstract

A novel underwater wireless sensor array network architecture is proposed. In this wireless sensor network, the maximum likelihood estimate of target location based on amplitude and phase of acoustic signatures is developed. Each local likelihood function can be partitioned into the amplitude and phase portion. For each sensor node is attached to a local sensor array which can performs maximum likelihood direction of arrival (DoA) locally. Global localization can combined with normalized LF and received target signal energy. Very promising simulation results demonstrated the feasibility of such a new approach for underwater wireless sensor network.