A new localization algorithm based on neural networks

Pelka, Mathias; Constapel, Manfred; Le Anh, Duc Tu; Hellbrück, Horst ORCID

Indoor localization plays a major role in a wide range of applications. To determine the location of a tag, localization algorithm is required. In the past, machine learning algorithms were difficult to implement in consumer hardware, but with the advent of tensor processing units, even smartphones are capable to use artificial intelligence to solve complex problems. In this paper, we investigate a machine learning algorithm based on neural networks and compare the result to a linear least squares estimator. We design and evaluate different neural networks. Based on our observation, the neural network delivers poor performance compared to the linear least squares estimator.

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Pelka, Mathias / Constapel, Manfred / Le Anh, Duc / et al: A new localization algorithm based on neural networks. Braunschweig 2018.

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