Underwater localisation is one of the main problems which must be addressed in subsea exploration, where no Global Positioning System (GPS) is available. In addition to the traditional underwater localisation systems, such as Long Base Line (LBL), new methods have been developed to increase navigation performance, flexibility, and to reduce deployment costs. For example, Range-Only and Single- Beacon (ROSB) is based on an autonomous vehicle which localises and tracks different underwater targets using slant range measurements carried out with acoustic modems. This paper presents different strategies to improve the 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 work advanced the understanding of accuracy that can be achieved by using ROSB target tracking methods with autonomous underwater vehicles.