Using spatial information to improve the accuracy of room-level localization

Signal strength-based method is widely adopted in localization nowadays. Because wireless signal strength is unstable, localization deviations are usually larger than one meter. For indoor localization systems, it is essential to provide room-level accuracy. However, with localization deviations larger than one meter, it is difficult to provide accurate room-level location. To solve this problem, we take advantage of spatial information and implement the concept of particle filters. Morever, we apply the Gaussian processes in the likelihood model to predict the mean and variance of the signal strength at any location and direction without the requirement of collecting the wholde training data. Unlike traditional methods of computing likelihood model, which merely consider the influence of location in signal strength, our system takes both location and direction into account. To avoid serious mistakes in localization results, we introduce a threshold in our system to restrict the transition of the tracked person’s room-level location and using spatial information to increase accuracy and efficiency of the constraint. We design a flow to determine whether the system should change the room location in all situations.o evaluate the results, we compare our system with other methods and apply frame error rate, word error rate and delay time as the performance metric. As a result, within tolerable delay increase, our system performs the best in the frame error rate and word error rate among all localization methods.