In English

Five-dimensional local positioning using neural networks

Fredrik Furufors
Göteborg : Chalmers tekniska högskola, 2017. 115 s.
[Examensarbete på avancerad nivå]

In this thesis, a method for real-time transmitter localization is evaluated. An existing system has acted as testbed for the evaluation. This system uses an electromagnetic transmitter and a receiver board with 16 antennas. The antenna values are used to recover the transmitters position and two angles, the five dimensions. The proposed solution is an inverse modelling feed-forward neural network, a multilayer perceptron, which is trained and evaluated with the use of the TensorFlow library. The project resulted in a purely software based estimator which requires no change to the testbed and can act as a drop in replacement for the previous algorithm. The new estimator has accomplished improvements in estimation speed (more than 100× faster), expansion of the volume in which the position can be recovered (27× larger), enlarged range of angles (10% per axis) and has improved the precision of the position estimates (error at the 95th percentile reduced to ~ 1/3 of the previous implementation). The new algorithm is a substantial improvement on the previous implementation, enabling new use cases for the system.

Nyckelord: Function approximation, Inverse modelling, Neural networks, Multilayer perceptron, Machine learning, Localization



Publikationen registrerades 2017-06-22. Den ändrades senast 2017-06-22

CPL ID: 250055

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