| With the rapid development of satellite navigation and positioning technology, the technique to obtain the spatial location attributes in outdoor environment has been developed. However, most of the time, people are still living in indoor environment, and the satellite navigation and positioning technology can not meet the needs of indoor positioning because of the current technical restrictions. Therefore, the specific positioning technique for indoor positioning should be studied.Since the late 1990s, many universities and research institutions have participated in the research of indoor positioning, such as Active Badges project developed by AT&T Cambridge, and Easy Living project made by Microsoft, and so on. Although the above mentioned projects have obtained some achievements, these positioning systems are still complicated, expensive and not scalable because new hardware design is required. On the other hand, Wireless Local Area Networks (WLAN) based on 802.11 have been deployed in the campus and office buildings. The WLAN positioning technique based on received signal strength is based on the principle that the received signal strength from access points varies with the distance between users and access points. Compared with the techniques of TOA and AOA, it has lower cost because it can make full use of the existed WLAN infrastructures. Therefore, the indoor positioning technology based on received signal strength in wireless networks is becoming more and more attractive.At present, the indoor positioning technique based on received signal strength has the following problems:(1).When there exist bigger obstacles in indoor environment such as elevator, the radio waves basically can not penetrate it. The contribution of RSS by transmission and reflection will be greatly reduced, the radio waves will reach the user by bypass diffraction. Therefore, the traditional path loss model is no longer applicable, and the improved model should be proposed.(2).The traditional path loss model of log distance assumes that the distance between users and access points has the exponential decline relationship with the received signal strength. However, a large number of experiments show that the model is not accurate. Therefore, the relationship between the distance and the RSS can not be summarized by a simple function. We can investigate the hidden principle of data by all kinds of evolutionary algorithms such as gene expression programming or GEP in order to further improve the accuracy of the model.(3).At present, how to optimize the location of access points to improve the positioning accuracy is still a difficult problem because of the complexity of indoor radio propagation environments. In fact, the correct and rational access points layout can improve the positioning accuracy remarkably.(4).The received signal strength is interfered by various types of noise, such as band interference among access points, sudden person walking and opening or closing of windows and doors, and so on. Therefore, these random disturbances of received signal strength will seriously decrease the accuracy of indoor positioning systems.(5). At real-time tracking stage, indoor positioning systems must calculate the location of users in a short time. Therefore, the number of received signal strength samples scanned by wireless network card is less, and the standard deviation of forecasted user location is larger, which will seriously decrease the performance and stability of indoor positioning systems.(6). In real life, people always move according to a certain paths, for example, walking from one side of corridor to the other side of it, but not passing through the wall, and so on. Therefore, all kinds of possible paths could be predefined, then the indoor positioning systems can make full use of these paths to further improve the positioning accuracy.In this paper, the above mentioned problems are analyzed and discussed in detail.In Chapter 1, the research background, research status, problems, and research subject of indoor positioning system based on wireless networks are introduced.In Chapter 2, the existing methods and principles of indoor positioning system are analyzed and summarized. Firstly, the principles of indoor positioning system are introduced. Secondly, technical classifications are summarized. Then, four positioning techniques are described. Finally, the indoor positioning algorithms based on WLAN are analyzed in detail, including propagation model method and location fingerprinting method.In Chapter 3, the related characteristics of indoor signal strength are analyzed in detail. Firstly, an indoor signal propagation model based on dominant path is presented, and the NLOS influence is further considered. Secondly, an indoor positioning model based on gene expression programming or GEP is proposed, and it makes full use of GEP to investigate the hidden non-linear relationship between the distance and received signal strength. Thirdly, a novel optimization model for access points location optimization is presented in which the Euclidean distance of received signal strength among all the sampling points should be maximized in order to increase the diversity of the signal strength. Then a differential evolution algorithm is utilized to optimize the proposed model. Finally, we proposed an overlap area matching technique which estimates the probability of user location by calculating the overlap area between the sampling Gaussian distribution and positioning Gaussian distribution.In Chapter 4, we propose three effective mechanisms to improve the positioning accuracy of existing indoor positioning algorithms. Firstly, five filtering methods, called Limit filter, Threshold filter, Max filter, MA filter and Kalman filter, are proposed and analyzed in order to decrease and filter the noise disturbance of received signal strength. Secondly, a Kalman filter tracking algorithm is proposed to filter the forecasted user location for less number of received signal strength samples at real-time tracking stage. Thirdly, an indoor positioning algorithm based on path tracking assistance is presented which makes the most of path information in indoor environment to optimize the searched subspaces of positioning algorithm. Finally, the above mentioned three mechanisms are integrated effectively to further improve the positioning accuracy of indoor positioning system.Chapter 5 mainly discusses and analyzes the design and implementation of indoor positioning system. 1).An indoor positioning system, named WiTracker, is introduced, which is developed by us. Then the technology route and algorithm flow of the WiTracker system are described. 2).The functional designs of the WiTracker system are depicted in detail and the main database tables are also introduced. 3).We deeply analyze the comprehensive performance of the WiTracker system and compare it with the Radar system. 4).Some influence factors to positioning accuracy are further discussed, including number of access points, and spacing between sampling point.At last, the author summarizes the work of this paper, and gives the further research directions. |
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