A Localization Algorithm for Underwater Acoustic Sensor Networks With Improved Newton Iteration and Simplified Kalman Filter

Abstract

Underwater acoustic localization is a crucial technique for most underwater applications. However, in highly dynamic marine environments, underwater acoustic localization faces many challenges, such as the stratification effect, the clock asynchronization, the node drift, and environmental noises. Concerning above problems, we propose a new underwater localization algorithm for mobile underwater acoustic sensor networks (UASNs). At first, the measurement biases are modeled as the combination of constant biases and random biases according to the physical mechanism of their generation and distribution characteristics in measured data. Then, an error-summation-incorporated Newton iteration (ESINI) algorithm is designed to compute the localization result along the direction of constant biases decrease, and a Taylor expansion is used to approach the actual localization result along the direction of random biases decrease. Subsequently, a simplified Kalman filter (SKF) fuses the two localization results and enhances the localization accuracy. In this way, the proposed algorithm effectively increases the accuracy of localization results without adding extra measurement. Finally, theoretical analyses, simulations, and lake experiments are provided to verify the proposed algorithm's effectiveness and noise resistance performance.