Abstract
In this paper, we present DeepMap, a deep Gaussian process for indoor radio map construction and location estimation. To address the shortcomings of existing Gaussian process based approaches, we present a DeepMap system, which employs deep Gaussian process for constructing received signal strength (RSS) radio maps and a Bayesian algorithm for online localization. We design a two-layer deep Gaussian process model to capture the relationship between the RSS space and the location space and provide an offline Bayesian training method to determine model parameters. A Bayesian fusion method using multiple APs is proposed for accurate location estimation. Experimental results verify the performances of DeepMap in a large indoor environment and validate its robustness with moderate training data.