 Acoustic positioning telemetry is nowadays widely used in behavioural ecology of aquatic animals. Data on the animal’s geographical location and its changes through time are used to study for instance movement patterns, habitat use and migration. The acoustic signals are detected by stand-alone receivers, allowing to collect huge amounts of data over long periods of time. However, large volumes of data might contain large errors. The traditional Time Difference of Arrival (TDOA) method used to calculate underwater positions, is a point-by-point approach: every position is calculated independently of all other positions. This method assumes that the acoustic transmissions follow a linear path. In many environments, this assumption is violated, for instance by reflections of the acoustic signal against hard surfaces, such as rock formations and concrete walls, or by diffractions around obstacles. Hence, acoustic positioning datasets usually require additional filtering. Unfortunately, the performance of the available filtering techniques is often unclear or ambiguous, especially when reflections occur. An alternative to the point-by-point approach, is a track-oriented approach, as used by YAPS (Yet Another Positioning System). This novel algorithm uses the information that is present in previous and subsequent positions, by combining a model of fish behaviour with Time of Arrival (TOA) of the signals on the receivers. In this study, we investigated the performance of two filtering techniques applied to positions provided by the Vemco Positioning System (VPS) in a highly reflective environment. We compared the unfiltered VPS positions with a standard filtering technique, making use of the Horizontal Positioning Error (HPE), and developed a new filter based on receiver cluster classification. Finally, we recalculated the positions with YAPS and compared the performance of this system to the two filtering techniques.