Localization systems using signal strength fingerprinting

• Main Author: Lee, Kung-Chung
• Language: English
• Published: University of British Columbia 2010
• Online Access: http://hdl.handle.net/2429/28750

The task of estimating the location of a mobile transceiver using the Received Signal Strength Indication (RSSI) values of radio transmissions to/from other radios is an inference problem. The fingerprinting paradigm is the most promising genre of methods studied in the literature. It constructs deterministic or probabilistic models from data sampled at the site. Probabilistic formulations are popular because they can be used under the Bayesian filter framework. We also categorize fingerprinting methods into regression or classification. The vast majority of existing methods perform regression as they estimate location information in terms of position coordinates. In contrast, the classification approach only estimates a specific region (e.g., kitchen or bedroom). This thesis is a continuation of studies on the fingerprinting paradigm. For the regression approach, we perform a comparison between the Unscentend Kalman Filter (UKF) and the Particle Filter (PF), two suboptimal solutions for the Bayesian filter. The UKF assumes near-linearity and imposes unimodal Gaussian densities while the PF does not. These assumptions are very fragile and we show that the UKF is not a robust solution in practice. For the classification approach, we are intrigued by a simple method we name the Simple Gaussian Classifier (SGC). We ponder if this simple method comes at a cost in terms of classfication errors. We compare the SGC against the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), two other popular classifiers. Experimental results present evidence that the SGC is very competitive. Furthermore, because the SGC is written in closed-form, it can be used directly under the Bayesian filter framework, which is better known as the Hidden Markov Model (HMM) filter. The fingerprinting paradigm is powerful but it suffers from the fact that conditions may change. We propose extending the Bayesian filter framework by utilizing the filter derivative to realize an online estimation scheme, which tracks the time-varying parameters. Preliminary results show some promise but further work is needed to validate its performance. === Applied Science, Faculty of === Electrical and Computer Engineering, Department of === Graduate