Double AUVs Cooperative Localization Based on Relative Heading Angle Optimization in Underwater Acoustic Sensor Networks

Localization by Autonomous Underwater Vehicle (AUV) is one of the main methods for locating target nodes in underwater acoustic sensor networks. Aiming at the problems of low localization coverage area and large localization error when AUV locates target nodes, a Double AUVs Cooperative Localization Based on Relative Heading Angle Optimization (DA-RHAO) algorithm is proposed in this paper. Firstly, Riemann integral is used to derive the relationship between localization coverage area and the relative heading angle during AUV movement. Secondly, the 3D target water is divided into multiple depth layers, and the relative heading angle between AUVs is selected according to the regional node density in each depth layer, which effectively enlarges localization coverage area of AUVs. Then, an AUV cooperative localization model is established to solve the coordinates of target nodes, which improves the localization efficiency and accuracy of the algorithm. Finally, the simulation results show that compared with the single AUV localization algorithm and AUV cooperative localization algorithm, the localization accuracy of DA-RHAO algorithm is improved by 26.89% and 12.56%, respectively.