A Novel 3-D Clustering-Based Indoor Localization System

• Main Authors: Wei-Han Tseng, 曾韋翰
• Other Authors: 林宗男
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/16181923694841125935

碩士 === 國立臺灣大學 === 電信工程學研究所 === 102 === Indoor localization develops fast in recent years and many systems or architectures are proposed in this topic. Among these methods, receive signal strength of Wi-Fi is a common feature because of its easy collection and no need for additional hardware. Fingerprintbased system of WLAN is a common model for its high accuracy in indoor localization. The fingerprint methods can be classified into two stages in this approach:(1)the offline radio map construction and model training, and (2)the online measuring and location estimating. It collects features and records them from reference points of whole indoor environment to construct radio map. Due to the high computation complexity of location estimating with the whole radio map. The reference points are classified into some smaller clusters, which is called clustering. It is a good solution for reducing computation complexity of system. In traditional clustering methods, such as k-means, support vector clustering, or affinity propagation, reference points in the same cluster may be disjoint or far apart from each other. With weighted-sum-based methods of location estimation, the location may be located outside of the region of cluster, such as a hollow square or the area between different floors in a building. We call it Prohibition Area Problem. To solve the problem, unlike the traditional methods, we combine the information of signal domain and spatial domain to gain the compaction of clusters in spatial domain. A Margin Propagation with Spatial Clustering (MPSC) is proposed with this new perspective. Besides, one of advantages for clustering with margins of support vector machine is reserving the distribution of original signal. Moreover, in the online stage, the measurements are assigned to the corresponding cluster, which is called cluster matching. In traditional methods, a fixed number of clusters is chosen beforehand. However, the suitable number of clusters is not equal at different locations. The proposed method adaptively adds clusters with similar probabilities to form larger cluster set, which is called adaptive multi-cluster matching. It increases the accuracy of cluster matching and improves the localization performance. Finally, a kernel-based weighted sum of reference points in corresponding cluster set is used to give estimated location. This 3-D indoor localization system achieves 1.15m as average error in BL Building of National Taiwan University.