Nowadays people have a larger demand on indoor positioning. Thanks to the different technical and technological evolutions, the development of the area is dynamic and diverse as we can costumize systems and applications to different types of places.
I had a project to create and implement an algorithm that can be used indoor as well and which can orient by certain propagation times and give the user’s position. From these times – which are adapted to the simulation environment – I choose the minimum of four. They mean the four fastest bases, so with the combination of their coordinates and the propagation times, the position and its environment can be determined. From this area I get some averages to set the final point so actually I use a filtered trilateration technique on the collected datas.
I got almost the same results to the different measuring arrangements. As I don’t investigate all the possible solutions of the equations, the final position is generated faster than if I use the original trilateration method, where I need to scan all of the solutions. The accuracy of my technique approaches the original version’s precision due to the well-chosen reference points. The difference is about from 15 to 20 cm which may describe the quantity of the filtering. The proper behaviour of the algorithm needs some other parameters, too, that I expound in the second part of my thesis.