| Summary: | The Global Positioning System (GPS) is a space based satellite navigation system. It provides location and time information in all weather, anywhere on the earth. Unfortunately GPS fails to give position indoors, because it requires a direct line of sight to several satellites. Indoor locating systems can thus not use GPS, because signal strengths are weakened or cancelled by building structures. So we need another technology for positioning indoors. Wireless indoor positioning systems are very popular in recent years. These systems are successfully used to asset tracking. By using ultrasound or lasers we can find accurate positioning, but this involves larger costs and energy requirements.Indoor wireless positioning based on received RF signal strength has gained more popularity for researchers in recent years. Wireless communication is a rapidly growing technology used in both home and business networking. Currently wireless networks are set up in institutes, hospitals, shopping malls, and airports and so on. Wi-Fi location determination is a technology; it utilizes existing Wi-Fi equipment such as those installed in personal computers, PDAs and mobile phones. The technology uses modulated Wi-Fi transmission signals to detect the presence of a device, which does not necessarily have to be connected to the network. The system is able to triangulate the position of the device based on the signals received from several access points. Some researchers implemented positioning algorithms to find the position indoors. In those algorithms some popular algorithms are signal strength mean value algorithm, K nearest neighbor’s algorithm, and Bayesian positioning algorithm. Before positioning, we can also measure the signal strength values in a reference point inside the building and use those values to build a database. The database contains coordinates of reference points, orientation and set of signal strength measurements linked to the access points. In positioning phase we can then measure the signal strength and compare those signals with an already built database for finding the position. This type of position finding is known as finger printing method.This paper provides an overview of the existing positioning techniques. The main aim of this thesis is to find the accurate position indoors. For finding the accurate position we are using the finger print database model. In addition to the finger print database model we are considering the walking speed of the user and the history of previous signal strength values. In this thesis we proposed a User Prediction Algorithm, using this algorithm we can find the position of object or user with less error and also we can solve the ambiguity problem to some extent.
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