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Top1. Introduction
Thanks to tremendous advances in wireless access techniques and mobile computing devices, intense interest has developed in location-based services. These technologies have made the dream of communication anytime and anywhere possible. People using a mobile device can surf the web as well as talk with their friends while they are moving all over the world. One of the most popular applications is the location-based service (LBS), such as geographical navigation for tourists or vehicles, advertising messages for local potential customers, 911 emergency services for subscribers, etc. The rapidly expanding demand for indoor/outdoor location-based services (LBS) has created a great need for high-precision location estimation techniques. In wireless networks, the most common technique used in location estimation is the RF site survey (Bahl & Padmanabhan, 2000; Youssef & Agrawala, 2004; Yin, Yang, & Ni, 2005; Youssef & Agrawala, 2005; Haeberlen, Flannery, Ladd, Rudys, Wallach, & Kavraki, 2004; Madigan, Elnahrawy, Martin, Ju, Krishnan, & Krishnakumar, 2005). As shown in Figure 1, the procedures of this technique are usually divided into an off-line phase and an on-line phase. The off-line phase is also called the training phase. Its objective is to construct a radio map by measuring the received signal strengths (RSSs) transmitted from access points (APs) at every location and recording the relation between each location and its measured RSSs. In the on-line phase, the location of mobile users (MU) is estimated through finding the best match of the RSSs between those measured from an MU and those recorded in the radio map.
Figure 1. RF site survey procedures
The most famous system using RF site survey is RADAR (Bahl & Padmanabhan, 2000). The positioning method used in the on-line phase mainly uses Euclidean distance to calculate the RSS difference between the MU measurements and prior data from the radio map. The Euclidean distance at a given location can be calculated by the equation 
where k, SSi, and SS’i respectively represent the number of APs, the ith AP’s RSS measured by MU, and the ith AP’s RSS recorded in the radio map. The positioning location is then calculated by averaging the k APs with the k minimum Euclidean distances.
Reference (Lim, Kung, Hou, & Luo, 2006) mentions that RSSs will vary frequently due to a number of factors such as multi-path fading, temperature and humidity variations, object relocations, and the presence of moving objects. Thus, the radio map will quickly become outdated and the accuracy of location estimations cannot be assured. As a result, frequent renewal calibrations are required to maintain up-to-date RSSs on the radio map, which incurs considerable labor cost.
Rather than constructing a radio map, in this paper we propose a positioning method which is calibration-free because the effect of wireless physical characteristics on the RSS is measured on-line by reference points (RP). We also provide an efficient and accurate positioning method by employing the minimum mean square error (MMSE) to estimate the user’s location. The proposed positioning method is implemented as a subsystem of the U-care project in the Chang Gung Health and Culture Village in Taiwan. The project aims to build a healthy, dignified, and happy environment for the aged as well as to slow the aging process and to enable a quick recovery, thus improving their quality of life.
The rest of the paper is organized as follows. First, we introduce related work on calibration-free positioning algorithms in Section 2 and then present our positioning algorithm in detail in section 3. The numerical results are provided in Section 4 and U-care project is introduced in Section 5. Finally, concluding remarks are presented in Section 6.