The study does not stop at theory: it presents a full algorithmic pipeline, from reading sensor data to delivering positions on a map. The algorithm is divided into clear stages, including reading and storing the input, computing bearing paths and quadrilaterals, filtering out unhelpful cases, reducing information to triads of sensors, and finally producing outputs that can be used for centroid-based position estimation. Each step is designed to keep the computation efficient and avoid unnecessary calculations in areas that are not relevant.

A practical feature is the definition of an Area of Interest (AOI) as a quadrilateral on the Earth’s surface. By focusing calculations only inside this AOI, the algorithm significantly reduces the number of possible combinations it has to consider, which is crucial when operating in real time with large sensor networks. Another parameter, the maximum angle difference, helps filter out bearing pairs that are too similar to provide meaningful triangulation, further improving performance.

For UnderSec, this efficient pipeline is important because it shows how a complex localisation task can be implemented in a way that is compatible with operational constraints. Large areas, multiple sensors and multiple transmitters can be handled without overwhelming computing resources, opening the door to real-time applications in the protection of critical and underwater infrastructures.