AN INDOOR POSITIONING TECHNIQUE BASED ON A FEED-FORWARD ARTIFICIAL NEURAL NETWORK USING LEVENBERG-MARQUARDT LEARNING METHOD

Pahlavani, P.; Gholami, A.; Azimi, S.

This paper presents an indoor positioning technique based on a multi-layer feed-forward (MLFF) artificial neural networks (ANN). Most of the indoor received signal strength (RSS)-based WLAN positioning systems use the fingerprinting technique that can be divided into two phases: the offline (calibration) phase and the online (estimation) phase. In this paper, RSSs were collected for all references points in four directions and two periods of time (Morning and Evening). Hence, RSS readings were sampled at a regular time interval and specific orientation at each reference point. The proposed ANN based model used Levenberg–Marquardt algorithm for learning and fitting the network to the training data. This RSS readings in all references points and the known position of these references points was prepared for training phase of the proposed MLFF neural network. Eventually, the average positioning error for this network using 30% check and validation data was computed approximately 2.20 meter.

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Pahlavani, P. / Gholami, A. / Azimi, S.: AN INDOOR POSITIONING TECHNIQUE BASED ON A FEED-FORWARD ARTIFICIAL NEURAL NETWORK USING LEVENBERG-MARQUARDT LEARNING METHOD. 2017. Copernicus Publications.

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