Robust indoor positioning in WLAN networks
- Autores: Juan Manuel Castro Arvizu
- Directores de la Tesis: Juan A. Fernández Rubio (dir. tes.), Pau Closas Gómez (codir. tes.)
- Lectura: En la Universitat Politècnica de Catalunya (UPC) ( España ) en 2017
- Idioma: español
- Tribunal Calificador de la Tesis: Gonzalo Seco Granados (presid.), Carlos Fernández Prades (secret.), Marc Ciurana Adell (voc.)
- Materias:
- Enlaces
- Tesis en acceso abierto en: TDX
- Resumen
Navigation and location technologies have been reaching in a major interest where Global Navigation Satellite System (GNSS) is mostly adopted. The limitation of this technology is that direct sky view is needed for reliable positioning. In indoor environments, however, it is difficult for GNSS technology to provide a reliable performance in positioning due to the signal attenuation and blocking caused by buildings and construction materials. For this reason, the growth in indoor applications has focused the research in new techniques for attempting mitigate the poor GNSS performance on this type of environments In the context of indoor positioning, multitude of emerging technologies for localization based on ultrasound, infrared, Ultra Wide Band (UWB), Zigbee, inertial navigation and other non-GNSS technologies have been proposed but special equipment is required and a large number of signal sources are needed. However, Wireless Local Area Network (WLAN) technology is widely used in indoor positioning. While the same requirements are also needed as the other technologies in order to improve the positioning accuracy, in terms of cost and ability, Wireless-based indoor location is widely used due to the already deployment of Anchor Points (AP) in urban and indoor areas. There are several methods for indoor positioning purposes e.g ToA (Time of Arrival), Received Signal Strength (RSS) measurements, AoA (Angle of Arrival), fingerprinting and so on. Most of the network-based location estimations use RSS measurements because almost all mobile devices are afforded to use this type of measurements. So, this thesis is centered in WLAN RSS-based positioning systems.
The first step for indoor positioning is the distance estimation between the user and the AP. Theoretical and empirical propagation channel models are used to translate the difference between the transmitted and Received Signal Strength into an estimated range. A Propagation channel model built the radio map and also report changes in the environment. There are several models in the literature to characterize this channel. Indoor RSS-based localization has become a popular solution, but standard techniques still consider a time invariant simple single slope path loss channel model with a priori known constant channel parameters. While some contributions considered the RSS-based localization problem using a single path loss model with unknown parameters, the general solution that considersa generalized distance dependent measurement model is an important missing point.
This thesis considers the two-slope channel model and proposes a robust indoor positioning solution based on a parallel architecture using a set of Interacting Multiple Models (IMM), each one involving two Extended Kalman filters (EKF) and dealing with the estimation of the distance to a given AP. Within each IMM, the two-slope path loss model parameters are sequentially estimated with Maximum Likelihood Estimate (MLE) to provide a robust solution. Finally, the set of distance estimates are fused into a standard EKF-based solution to mobile target tracking. In addition, the benchmarks derived in this thesis to evaluate the performance of our IMM-EKF algorithm are the Cramér Rao Lower Bound (CRLB) and the Posterior Cramér Rao Lower Bound (PCRLB) providing a guidance in the improvement of the experimental design. The CRLB is used to assess the estimation of model parameters and the PCRLB for tracking solution. This, combined with a path-loss exponent estimation, the channel calibration algorithm is validated with an online range estimation.
The central theme throughout this thesis is to develop a completely online two-slope channel calibration and, simultaneously, a mobile target tracking algorithm. The performance of the method is assessed through realistic computer simulations and validated with real RSS measurements obtained from experimental tests in a typical office environment.
