Study On WLAN Based Indoor Location Estimation Technology

With the widespread of wireless networks and development of pervasive computing, location based services (LBS) have attracted more and more attention and shown great energy in many applications, such as emergency, medical care and customized information delivery. Location estimation is a prerequisite and key to implement pervasive computing applications and LBS.However, existing location estimation technology, especially in the indoor environments, cannot satisfy the needs of pervasive computing applications in system cost, application area and portability, which limits the popularization of LBS in common people. In recent years, the indoor location estimation technology based on WLAN and location fingerprinting has become a hot research topic in the field of pervasive computing and location awareness for the advantages that it works in a more wide area, can be implemented simply in software and doesn't cost too much.Aiming at existing problems and requirements in location estimation accuracy and energy consumption of mobile device, the thesis makes a deep and systematic study, and proposes corresponding solutions using theory of artificial intelligence and data mining. Through algorithm comparison and experimental analysis, the validity and practicability of the proposed solutions are demonstrated. The major contributions of this dissertation are:(1) With the location fingerprints measured in a practical WLAN, the thesis analyzes some characteristics of radio signal propagation in indoor environments from positioning point of view. Based on this, a mathematical model of average error of localization is proposed.In WLAN and location fingerprint based location estimation technique, the sampling grid spacing, the number of access points, and the interference of environment are key factors for average error of location estimation. However, there isn't a general strategy on how to determine these parameters. They are mostly determined by experience. The mathematical model formally describes the key factors for errors in fingerprint based location estimation. Through emulation experiments, relationships between the average error and the number of access points, the size of sampling grid and environmental factors are analyzed, which helps to design location estimation algorithm and deploy location estimation system.(2) According to the idea of multi-source information fusion, the thesis presents a location estimation method based on Dempster-Shafer evidence theory.Due to the interference from noises of complex indoor environments, data of location fingerprints always contain uncertainty. Improving location estimation accuracy is one of important research focuses. The method considers access points as information sources of received signal strength, and assigns different belief to information sources. The contribution of access points to location estimation results is amply described and differentiated. To choose the information sources, an access point selection method proposed by the thesis, that is, the maximum matching method is used. Compared with existing location estimation methods that often used, the proposed method can estimate user location more efficiently and get higher location estimation accuracy.(3) This thesis proposes a Gauss mixture model (GMM) based location fingerprint clustering algorithm.As more and more clients choose small, self-maintained devices which heavily depend on battery power, how to reduce computation cost in location estimation and save energy is also a very important problem. The proposed clustering algorithm uses a Gauss mixture model to represent location fingerprint clusters. By taking account of the probability distribution characteristic of received signal strength, the algorithm overcomes the shortcoming of other location fingerprint clustering algorithms which only consider the similarity of signal strength values, and thus alleviates the sensitivity to noisy data. Moreover, parameters in the algorithm are easy to determine. Experimental results show that the clustering algorithm can effectively reduce computation cost of location estimation algorithm and decrease energy consumption of mobile devices.(4) Oriented to pervasive computing applications, a prototype of indoor location estimation system based on WLAN and location fingerprinting technique is designed and implemented. All experiments are conducted in a practical indoor WLAN environment. Experimental results show that the proposed solutions not only improve location estimation accuracy but also reduce computation cost so that the practicability of location estimation system is enhanced.