In recent years, indoor localisation and movement tracking of people and objects has generated interest for a variety of applications ranging from transport to health care. We present a localisation network designed to track people in an indoor environment. The localisation network consists of static nodes placed at predetermined locations in a building. Users carry a mobile node to track their current position. The mobile node has onboard motion sensors to detect a person's heading direction and motion state. A dynamic tracking mode was used to determine a person's position. The dynamic tracking model was implemented using a Multi-Hypothesis Estimation algorithm. The dynamic tracking model determines the mobile node's position by using the mobile node's proximity to static nodes, mobile node's motion sensor information and the building's floor-plan. We found that by using a multi-hypothesis estimation algorithm, robust localisation accuracy, could be achieved in real-time. The position resolution of the localisation network was found to have a maximum error between Im and 3.5m. Further work involves extensive testing the localisation network with multiple mobile nodes and over a larger test region. Other areas involve investigating how multiple mobile nodes placed on a user can be used to improve the estimate of the user's position.