Robust multi-model mobile target localization scheme based on underwater acoustic sensor networks

Underwater mobile target localization based on underwater acoustic sensor networks (UASNs) is a critical application in marine fields. However, the mobility of underwater targets, asynchronous reception of localization information, stratification effects, and unknown measurement losses make UASN-assisted mobile target locali-zation difficult. To address these issues, this study proposes a robust multi-model mobile target localization scheme (RMML) based on UASNs. The RMML suggests a simplified approach for optimal localization reference node selection based on the Crame ' r-Rao lower bound. This approach improves the localization accuracy of a mobile target by periodically selecting nodes that provide high-quality localization references. Using the local-ization reference provided by the selected nodes, a robust mobile target localization algorithm is developed based on an interacting multiple model and unscented Kalman filter. In this algorithm, a multipoint prediction method and ray tracing method are jointly proposed to improve the target state estimation accuracy under the asynchronous reception of localization information and the stratification effect. Maximum a posteriori proba-bility estimation is used to estimate measurement loss, and pseudo-residuals are constructed based on the esti-mated measurement loss to improve the localization accuracy and robustness of the algorithm. Finally, extensive simulations and experiments are performed to verify the effectiveness of the RMML.