The ARGOS service was launched in 1978 to serve environmental applications including oceanography, wildlife tracking and maritime safety. The system allows for worldwide positioning and data collection of Platform Terminal Transmitters (PTTs). The positioning is achieved by exploiting the Doppler shift in the carrier frequency of the messages transmitted by the PTTs and recorded by dedicated satellite-borne receivers. Over the last twenty years, the transmission power has decreased significantly and platforms have been used in increasingly harsh environments. This led to deliver a greater number of low quality locations while users sought to identify finer platform behavior. This work first focuses on the implementation of a more efficient location processing to replace the historical real time processing relying on a Least Squares adjustment. Secondly, an offline service to infer locations with even higher accuracy is proposed. The location problem is formulated as the estimation of the state vector of a dynamical system, accounting for a set of admissible movement models of the platform. The exact determination of the state posterior pdf displays a complexity growing exponentially with time. The Interacting Multiple Model (IMM) algorithm has become a standard online approach to derive an approximated solution with a constant computational complexity. For offline applications, many sub-optimal multiple model schemes have also been proposed. Our methodological contributions first focused on extending the framework of the IMM filter so as to handle a bank of models with state vectors of heterogeneous size and meaning. Second, we investigated a new sub-optimal solution for multiple- model smoothing which proves to be less computationally expensive and displays markedly better performance than equivalent algorithms. The ARGOS location processing was rewritten to include the IMM filter as real time processing and the IMM smoother as offline service. We eventually analyzed their performances using a large dataset obtained from over 200 mobiles carrying both an ARGOS transmitter and a GPS receiver used as ground truth. The results show that the new approaches significantly improve the positioning accuracy, especially when few messages are received. Moreover, the algorithms deliver 30% more positions and give a systematic estimation of the location error.
