An effective calibration method for wireless indoor positioning system with mixture Gaussian distribution model

Abstract

The quality of Received radio signal strength (RSS) is essential for obtaining higher accuracy in the fingerprint-based positioning system. However, due to the variation of the RSS which is caused by multi-path propagation or moving objects, such a precise positioning system is difficult to achieve. To address the above issues, a new calibration method is presented. The method consists of two parts: offline training part and online positioning part. A high accurate model is obtained in the offline training part by a neural network, and the unknown position is determined in the online part using the model which is trained in the previous part. In the offline part, a Gaussian fitting is first applied to the distribution of the obtained RSS data, and mixture Gaussian calibration method is adopted when more than one significant peak is occurred in the Gaussian model. In the online part, k-times measurement is adopted to enhance the quality of the RSS data. A set of experiments in an indoor positioning scenario is conducted. The experimental results show that in the offline part, the proposed mixture Gaussian calibration method can significantly improve the accuracy from 3.2m to 2.5m for 80% test points, and in the online part, the proposed k-times measurement narrows down the localization error from 4.3m to 2.4 for 80% test points. For that, no hardware modifications are required.