LAUSR

Underwater localization is one of the main problems that must be addressed in subsea exploration, where no global positioning system (GPS) is available. In addition to the traditional underwater localization systems, such as long base line (LBL), new methods have been developed to increase the navigation performance and flexibility and to reduce the deployment costs. For example, range-only and single-beacon (ROSB) is based on an autonomous vehicle that localizes and tracks different underwater targets using slant range measurements carried out with acoustic modems. This paper presents different strategies to improve ROSB tracking methods. The ROSB target tracking method can be seen as a hidden Markov model (HMM) problem. Using Bayes’ rule, the probability distribution function of the HMM states can be solved by using different filtering methods. Here, we present and compare different methods under different scenarios, both evaluated in simulations and field tests. The main mathematical notation and performance of each algorithm are presented, where best practice has been derived. From a methodological point of view, this paper advanced the understanding of accuracy that can be achieved by using the ROSB target tracking methods with autonomous underwater vehicles.