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Wireless Indoor Localization Problem with Artificial Neural Network

EasyChair Preprint no. 6070

4 pagesDate: July 13, 2021


Positioning in indoor application is a challenging problem with GPS signals. Because the obstacles such as doors and walls weaken the GPS signal amplitudes, indoor positioning results are not satisfying with global positioning system. Indoor positioning may be critical for a variety of applications such as, detecting number of people, locating criminals in bounded regions, and obtaining the number of users in a special area. The Wi-Fi signal strength may be a key point to solve this problem. With several routers, the received Wi-Fi signal power information may use to determine the indoor localization with using the information of routers location. In this work, Multi-Layer Perceptron (MLP) neural network method is proposed that can be implemented in monitoring and tracking devices. In the end the theoretical background and simulation results are shared. Both k-fold cross validation and hidden neuron numbers are changed in the simulation then the results are compared.

Keyphrases: Indoor positioning/localization, MLP, Multi Layer Perceptron, neural network, positioning with Wi-Fi signals, User Localization

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Furkan Kardaş and Ömer Karal},
  title = {Wireless Indoor Localization Problem with Artificial Neural Network},
  howpublished = {EasyChair Preprint no. 6070},

  year = {EasyChair, 2021}}
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