MMSE location estimation using multiple items of sensed information in indoor environments

Abstract

Receiving radio waves directly from satellites is difficult in an indoor environment, making accurate location estimation by satellite signals difficult. Since mobile communication propagation channels suffer from fading and shadowing, estimating accurate locations in an indoor environment by using only the received signal power of a radio wave is also difficult. In addition to using the received signal power, we have proposed the use of multiple items of sensed information for location estimation by multiple regression analysis, and we have shown that this method can reduce estimation errors compared with the conventional one. However, information with the exception of the received signal power does not necessarily change linearly with distance. In this paper, we propose a minimum mean square error (MMSE) location estimation method. This method estimates the location that minimizes the mean square error between multiple items of sensed information and a constructed database that provides the relationship between the location and the multiple items of information. Our method is experimentally validated and compared with the conventional linear regression method. Our method was found to reduce the standard deviation of the location estimation error to less than 1/4, and the probability that the estimated distance is equal to the actual distance is about three times as high compared with the linear regression method.