A Probabilistic Approach to Mobile Location Estimation within Cellular Networks

Mobile location estimation is becoming an important value added service for a mobile phone network. It is well-known that GPS can provide an accurate location estimation. But it is also a known fact that GPS does not perform well in urban areas like downtown New York and cities like Hong Kong. Many mobile location estimation approaches based on cellular networks have been proposed to compensate the problem of the lost of GPS signals in providing location services to mobile users in metropolitan areas. Among different kinds of mobile location estimation technologies, only the class of signal strength based algorithm which estimates the location of the mobile station (MS) by signal strength received from the nearly base stations (BSs) can be applied to different kinds of cellular networks, and therefore, it is a more general solution. In this paper, we design a directional propagation model, the Modified Directional Propagation Model (MDPM), which makes use of a common signal propagation model to perform location estimation. We test MDPM with real data taken in Hong Kong and experimental results show that MDPM outperforms other existing location estimation algorithms among different kinds of terrains and environmental factors.