Location performance enhancement with recursive processing of time-of-arrival measurements

This work deals with the development of pre-filtering techniques for low-cost devices using high data rate communications. Many positioning algorithms have been recently revisited in a centralized architecture scenario, where a cheap Mobile Sensor is surrounded by N Base Stations. The overview of the system is composed of two blocks: a Smoothing Filters and a Positioning Block. In the Smoothing Filters block, different algorithms such as smoothing filter, Recursive Least Squares and Maximum-Likelihood are developed to process multiple Time-of-Arrival measurements before triangulating the position of the Mobile Sensor. The positioning algorithms are the Taylor Series, Direct Method and Spherical Interpolation. All in all, it is shown that the recursive processing of multiple measurements at the input of the positioning algorithm improves not only the accuracy of the triangulated position, but also the robustness of the positioning algorithms. We also explain why the very good results given by the Maximum-Likelihood should only be seen as a lower-bound of the system.