| Summary: | 碩士 === 國立交通大學 === 資訊學院碩士在職專班資訊組 === 96 === Due to the fast growing in the user population of Internet and Wireless LAN, location-aware services and systems become a popular topic. Although GPS systems are widely deployed for traveling and driving guidance, it is not suitable for indoor environment. Further, it does not provide sufficient accuracy for face to face applications. Besides, current GPS system was solely designed for positioning purpose, so it needs to be combined with wireless communication service to implement location-aware function for mobile computing purposes. Because of these GPS disadvantages, quite a few indoor positioning schemes were proposed in the past years, but most of them are either expensive or featuring low accuracy.
In this thesis, we proposed an Intelligent WiFi LAN Positioning System which was implemented on the application layer to position the mobile station in indoor environment. Since it was implemented on application layer, it does not need any change of wireless equipments. Further, it is intelligent because it has the machine learning capability, which is quite useful for extending the positioned area and system maintenance. The basic idea of this learning capability is that the positioned locations can be reused as new samples for future positioning. But there are some potential problems for using all of these positioned locations as future samples. We will discuss the problem and provide our solution in this work.
We also introduce schemes to improve the accuracy, performance and flexibility for our system. After the system has been trained for a certain period, the accuracy will be improved, but there will be a large number of samples in the Location Tables. Searching through the table one by one will be a time consuming task on Location Server, in our system, searching all samples is required only in the beginning or in few special cases.
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