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Title: Algorithms and performance analysis for indoor location tracking systems
Authors: JIN YUNYE
Keywords: Indoor localization, pedestrian tracking, performance analysis, algorithms, location fingerprinting, dead-reckoning
Issue Date: 27-Jul-2011
Citation: JIN YUNYE (2011-07-27). Algorithms and performance analysis for indoor location tracking systems. ScholarBank@NUS Repository.
Abstract: The ability to accurately track a user?s location in the indoor environment has many applications in the healthcare, logistic, and entertainment industries. This thesis makes a threefold contribution to the realization and analysis of practical indoor location tracking systems. First, we propose an efficient channel-impulse-response-based (CIR-based) location fingerprint, derived from receiver channel estimation results. Logarithmic transformation is applied to ensure that each element in the fingerprint vector contributes fairly towards the location estimation. Simulation results show that, with the same number of access points and the same amount of training efforts, the proposed method significantly outperforms the existing fingerprint-based methods in the literature. It is also robust to the environmental changes caused by the presence of a crowd of human bodies. Second, we derive the exact theoretical expressions of both the online error probability density function (PDF) and region of confidence (RoC) for a generalized location fingerprinting system. Computations of both terms require the joint PDF for the location and the online signal parameter vector, which is practically unknown. We therefore propose to approximate this joint PDF by nonparametric kernel density estimation using the training fingerprints, without extra calibration efforts. Experimental results show that, the proposed scheme predicts the empirical error PDF closely for the two most popular location fingerprinting methods, namely, the K nearest neighbour (KNN) and the probabilistic approach. The third contribution includes two different approaches that we propose to realize a robust pedestrian tracking system using mobile devices with low cost sensors. The first approach fuses the estimates of a dead-reckoning (DR) system with the measurements of a sparsely deployed ranging infrastructure, using a particle filter (PF). Experimental results show that this approach significantly reduces DR tracking error even when (i) initial location is unknown, (ii) range measurements have errors, (iii) range updates are intermittent and sparse both temporally and spatially. The second approach fuses the estimates of two DR modules, carried by the same pedestrian and mounted with stable relative displacement, through a maximum a posteriori estimation scheme. Experimental results show that, the proposed scheme delivers robust tracking performance, with significantly smaller average error compared to traditional DR methods, when using (i) two DR modules, each with a single orientation sensor and arbitrary device orientation, (ii) one DR module, with two different orientation sensors and fixed device orientation.
Appears in Collections:Ph.D Theses (Open)

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