In this thesis, an indoor localization method using on-line independent support vector machine (OISVM) classification method and under-sampling techniques is proposed. The proposed positioning method is based on the received signal strength indicator (RSSI) of Wi-Fi signals. A new under-sampling algorithm is developed to address the imbalanced data problem associated with the OISVM, and a kernel function parameter selection algorithm is introduced for the training process. The time complexity of both the training process and the prediction process are decreased. Comparative experimental results indicate that the training speed and the prediction speed are improved by at least 10 times and 5 times, respectively. Furthermore, through on-line learning, the estimation error is decreased by 0.8m. Such an improvement makes the proposed method an ideal indoor positioning solution for portable devices where the processing power and the memory capacity are limited.;A new Particle Filter (PF) scheme for indoor localization using Wi-Fi received signal strength indicator (RSSI) and inertial sensor measurements has also been presented. RSSI is affected significantly by multipath fading, building structure and obstacles in indoor environments. The information provided by inertial sensors combined with the proposed particle filter are used to develop a positioning algorithm supporting a smooth and stable localization experience. To differentiate similar fingerprints, a single-hidden layer feedforward networks (SLFNs) is used to model the multiple probabilistic estimations and to improve the performance of the PF. A new initialization algorithm using Random Sample Consensus (RANSAC) has also been presented to reduce the convergence time. Experimental measurements were carried out to determine the performance of the proposed algorithm. The results indicate that the positioning error falls to less than 1.2 (m).
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