Feedback-Based Target Localization in Underwater Sensor Networks: A Multisensor Fusion Approach

Abstract

This paper investigates the problem of target localization in underwater sensor networks, subjected to limited measurement range and accuracy. The localization process is mainly divided into two phases, i.e., distance estimation and position solving. In the first phase, some sensor nodes cannot acquire the direct distance measurements of target, due to the high-dynamic and strong-noise characteristics of underwater environment. Based on this, we formulate the distance measurement as a closed-loop control problem, and then a proportional-integral estimator is designed for sensors to acquire the distance information through indirect measurements. With the estimated distance information, a consensus-based unscented Kalman filtering (UKF) algorithm is proposed in the second phase to localize the target, where direct and indirect measurements are fused to reduce the influence of malicious data. Moreover, stability conditions are provided to show that the distance estimator can stabilize the closed-loop system, while the boundedness analyses are demonstrated to guarantee the localization accuracy. Finally, simulation results reveal that the proposed distance estimator can extend the measurement range of sensors by comparing with the single direct measurement. Meanwhile, the consensus-based UKF algorithm can effectively improve the localization accuracy.