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

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.