Abstract
In this paper, we present statistical models and ML (maximum likelihood) positioning algorithms using received signal powers in sensor networks. The purpose of this study is to develop the indoor positioning system with utilizing the IEEE Std 802.15.4 [1] based wireless sensor network.The distance between nodes can be presumed by using the RSSI (received signal strength indicator) of the wireless data communication in the sensor network. Therefore, it becomes possible to presume the position of the sensor node by using the RSSI from a certain sending source, namely from the base station with already measured position.The variation of the RSSI is large because of the influence by the measurement environment. Therefore, it is necessary to acquire a lot of the RSSI data to improve the position estimation. In this paper, we present the models of RSSI of radio signal propagation applying the Rayleigh distribution and gamma distribution. We propose a positioning algorithm based on the ML method from the probability density functions of the signal powers.
