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The objective of this paper is to propose a reliable and accurate positioning method, which provides location estimates for the mobile user in hybrid wireless network (IEEE802.11/WiFi, IEEE802.16/WiMAX and LTE technologies). The developed data fusion algorithm utilises measurements and features such as Time of Arrival (TOA) measurements and multiple input, multiple output (MIMO) antennas and wireless links between mobile users in order to enhance the mobile-centric positioning accuracy. Therefore, in this paper not only a new reliable concept of mobile user positioning is proposed, but also it is applied to case scenarios where various wireless networks are present. Results satisfy the FCC requirements for the mobile-centric positioning solution.

Large traffic network systems require handling huge amounts of data, often distributed over a large geographical region in space and time. Centralised processing is not then the right choice in such cases. In this paper we develop a parallelised Gaussian Mixture Model filter (GMMF) for traffic networks aimed to: 1) work with high amounts of data and heterogenous data (from different sensor modalities), 2) provide robustness in the presence of sparse and missing sensor data, 3) able to incorporate different models in different traffic segments and represent various traffic regimes, 4) able to cope with multimodalities (e.g., due to multimodal measurement likelihood or multimodal state probability density functions). The efficiency of the parallelised GMMF is investigated over traffic flows based on macroscopic modelling and compared with a centralised GMMF. The proposed GMM approach is general, it is applicable to systems where the overall state vector can be partitioned into state components (subsets), corresponding to certain geographical regions, such that most of the interactions take place within the subsets. The performance of the paralellised and centralised GMMFs is investigated and evaluated in terms of accuracy and complexity.