A cooperative positioning algorithm based on adaptive selection and factor graph

The cooperative positioning technology based on Autonomous Underwater Vehicles (AUVs) is one of the most rapidly developing underwater positioning technologies in recent years, it can complete complex underwater tasks that are difficult for a single AUV to complete, and has broad development prospects. Due to the complex underwater environment, fuzzy time-varying noise may be generated, which may also lead to changes in the system structure, the number of leader AUVs is numerous and the quality is complex. These factors can lead to a decrease in the accuracy and efficiency of traditional algorithms. To address these issues, we propose a cooperative positioning algorithm based on dynamic structure-based adaptive selection and Factor Graph (DS-AOSFG). Systems with dynamic topology can effectively cope with structural changes. In response to fuzzy time-varying noise, we introduce an adaptive filter module. In response to the problem of many leader AUVs and mixed quality, we introduce a node optimization selection module and improve the traditional algorithm through the factor graph algorithm. The final evaluation indicates that our DS-AOSFG method outperforms other algorithms in terms of positioning accuracy and speed.