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Multi-Layer Perceptron Neural Network and nearest neighbor approaches for indoor localization | IEEE Conference Publication | IEEE Xplore

Multi-Layer Perceptron Neural Network and nearest neighbor approaches for indoor localization


Abstract:

Most range-free techniques for indoor localization depend on the received signal strength (RSS) fingerprints. Their performances are relied to the structure of the consid...Show More

Abstract:

Most range-free techniques for indoor localization depend on the received signal strength (RSS) fingerprints. Their performances are relied to the structure of the considered indoor environments. We consider in this paper RSS-based methods: Multi-Layer Perceptron Neural Network (MLPNN), and K-nearest neighbor (KNN), and compare their performance under the same indoor environment. One of the advantages focused by the choice of these techniques is their robustness against external disturbances that may affect the received RSS signal. Moreover, we propose a new metric to enhance the performance of the KNN method, called d-nearest neighbor. In order to test the different techniques, we build a heterogeneous fingerprint database with different resolutions. The obtained results show the efficiency of the proposed enhancement in the case of a heterogeneous high resolution database.
Date of Conference: 05-08 October 2014
Date Added to IEEE Xplore: 04 December 2014
Electronic ISBN:978-1-4799-3840-7
Print ISSN: 1062-922X
Conference Location: San Diego, CA, USA

References

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