Position estimation in indoor localization system

Graduation date: 2015Indoor positioning systems can be used for many applications such as indoor navigation,\ud emergence response, asset monitoring, and shopper assistance. Due to the weak received signal and multipath reflection, the global positioning system (GPS) generally does not work in indoor environments. There are a variety of radio frequency (RF) signals and systems available for indoor localization, e.g., radio-frequency identification (RFID), cellular network, Bluetooth, WiFi, and ultrawideband (UWB) systems. For high-precision localization using RF signals, commonly used techniques include time-of-arrival (TOA) and time-difference-of-arrival (TDOA). Although various aspects of TOA and TDOA systems have been studied extensively, new techniques are still needed to improve two key position estimation aspects: accuracy and complexity.\ud \ud In the first part of this dissertation, we focus on position estimation methods assuming line-of-sight (LOS) propagation. Anchor layout is an important area that affects localization performance. Generally the Cram\'{e}r-Rao lower bound (CRLB) can be used to find the optimal anchor layout. However, it is computationally expensive and not suitable for fast deployment. We propose an incremental anchor layout method (ICALM) based on the largest range measurement change criterion, which is very easy to implement.\ud \ud For TOA systems, an improved method of moments (IMOM) algorithm is proposed to improve the estimation accuracy at the expense of a slightly increased computational complexity. For TDOA systems, we propose a maximal likelihood (ML) based coarse position estimation method to provide the initial position for the nonlinear least squares (NLLS) method. The goal of this proposed method is to substantially increase the stability of the NLLS method. In order to reduce estimation complexity, we propose a nonlinear expectation maximization (NLEM) based estimator. This estimator transforms the high-dimensional estimation problem into several 1-dimensional problems, which does not need any matrix manipulations and is much simpler to implement than the NLLS and ML methods.\ud \ud In practice, none-line-of-sight (NLOS) links often exist. In the second part of this dissertation, we focus on methods for NLOS mitigation. When all the range measurements suffer from severe NLOS errors, no methods could work well without additional information. However, when only part of the range measurements suffer from NLOS propagation, and the LOS range measurements are sufficient for position estimation, it is possible to improve the accuracy without any \emph{a priori} information. We propose an improved least median squares (ILMedS) algorithm, which uses the residue to weight all the anchors and adaptively searches for the largest group of the reliable links for final estimation. It greatly decreases the probability of reaching the outliers and increases the accuracy. At the same time, all the methods developed for the LOS scenarios can be directly applied as the core estimator.\ud \ud Since ILMedS needs to calculate a location estimate for each subset, its computational complexity is high. We propose a particle filter based position estimation (PFPE) method for NLOS mitigation, which uses particles to represent the potential target. Each particle utilizes the range measurements to update its position. It is much easier to implement than the ILMedS method, while their performances are very similar