An Indoor Positioning System (IPS) using grid model.

INTRODUCTION

There are mainly three categories of geographic positioning systems to determine or track a user's location, which have been designed and proposed over the years. These systems are mainly three categories: Global Positioning System, Wide-area Location System and Indoor Positioning System. Global Positioning System (GPS) receives signals from multiple satellites to determine the physical location of a user. The limitation for this system is that it is inefficient for indoor use; alongside in urban areas it often possesses difficulties in receiving signals where high buildings shield the satellite signals (5). Wide-area location systems are mainly based on cellular networks that involve measuring the signal strength, the angle of signal arrival and/or the time difference of signal arrival. The positioning information in wide-area location systems is highly limited by the cell size or cell coverage (11). Several approaches have been proposed for indoor location sensing or indoor positioning system such as infrared sensing, radio frequency, ultrasonic and scene capture analysis (12). There are also a few technologies to use within indoor areas, such as GPS psudolite, ultrasonic and cellular-based systems, which need considerable supporting devices and facilities. Each of these methods has their own advantages and disadvantages. Some are expensive to implement, while others are not very accurate. The Active Badge is the first location system (14). Radar, well-known approach, is an RF system for locating and tracking users within large structures (1), (2). There are two approached are presented, one is an empirical method and a signal propagation model. This procedure determines user location by combining signal strength measurements with signal propagation models. RF signal strength within building is affected by multipath propagation effects and absorption, resulting in non-linear behavior. The results show that the empirical method is superior in terms of accuracy with median resolution in the range of about 3 m and the signal propagation model has 4.3 m accuracy (median), but it makes deployment easier.

The applications of indoor positioning are many, for instance, location-finding, indoor robots, inventory tracking, security, etc. Various methods do exist for indoor positioning; however, the concern rises if the indoor positioning is addressing the issue of flexibility, cost and accuracy. While the demand for location-detection in indoor application is growing, so far there is no commonly agreed way to determine accurate position in indoors. In the recent past years, many researches have been conducted on indoor positioning system using mobile devices (10), (7). The earliest one, the Active Badge system, has used the infra-red network. A Radio Frequency or RF and an ultrasound are used in Cricket location-support system (9). Most of these systems are based on an analysis of receiver signal-to-noise ratio and determine user location by combining signal strength measurements with signal propagation models (8), (6). For RF-based techniques, additional hardware is not required for user location determination, which uses only the possible existing wireless data network. Neural Network is one of the methods while the flexible modeling and learning capabilities are not required the detailed knowledge of the access point locations and of the building characteristics (3), (4). Using mobile computing devices and wireless LANs to determine the location of a device is becoming a popular application, particularly the WLAN that are based on the IEEE 802.11b standard. A wireless local-area network (WLAN) based positioning system has distinct advantages over all other systems. At first, it is an economic solution because in many areas usually the WLAN network already exists as part of the communication infrastructure. …