Indoor localization techniques using wireless network and artificial neural network
This research focuses on improving indoor localization using wireless network and artificial neural network (ANN). This involves strategic study on wireless signal behavior and propagation inside buildings, suitable propagation model to simulate indoor propagation and evaluations on different locali...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/78448/1/AimanIbrahimMFKE2017.pdf |
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| Summary: | This research focuses on improving indoor localization using wireless network and artificial neural network (ANN). This involves strategic study on wireless signal behavior and propagation inside buildings, suitable propagation model to simulate indoor propagation and evaluations on different localization methods such as distance based, direction based, time based and signature based. It has been identified that indoor signal propagation impairments are severe, non-linear and custom to a specific indoor location. To accommodate these impairments, an ANN is proposed to provide a viable solution for indoor location prediction as it learns the location specific parameters during training, and then performs positioning based on the trained data, while being robust to severe and non-linear propagation effects. The versatility of ANN allows different setup and optimization possibilities to affect location prediction capabilities. This research identified the best feedforward backpropagation neural network configuration for the generated simulation data and introduced a new optimization method. Indoor-specific received signal strength data were developed with the Lee’s in-building model according to a custom indoor layout. Simulation work was done to test localization performance with different feedforward backpropagation neural network setups with the generated received signal strength data as input. A data preparation method that converts the received signal strength raw data into average, median, min and max values prior to be fed into the neural network process was carried out. The method managed to increase location prediction performance using feedforward neural network with two hidden layers trained with Bayesian Regularization algorithm producing root mean squared error of 0.0821m, which is 50% better in comparison to existing research work. Additional tests conducted with six different relevant scenarios verified the scheme for localization performance robustness. In conclusion, the research has improved the performance of indoor localization using wireless network and ANN. |
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