Bayesian radio map learning for robust indoor positioning

The uncertainty of radio propagation results in large errors in positioning systems based on the received signal strength (RSS). Especially in an indoor environment, the RSS distribution map, so called radio map, has a very complicated form due to numerous site-specific parameters. Therefore, modelling the radio map is a critical task for RSS based positioning systems. Researchers usually obtain an accurate radio map by measuring the RSS at a number of reference points. But in this way too many calibration efforts should be spent to guarantee a fine radio map accuracy. In this paper, a calibration-free radio map learning framework is proposed. In this framework, the system starts with a very simple and coarse radio map model, such as a radial model with default parameter values. A more accurate model is then obtained by learning the unlabelled online RSS data. The Expectation-Maximisation (EM) algorithm is used to calculate the posterior maximum likelihood (ML) of radio maps. Besides, we extend the standard EM algorithm by integrating expert knowledge of radio propagation. By applying the proposed algorithms in real-world data sets, we demonstrate that an accurate and robust radio map can be learned without requiring any calibration data.