Non-line-of-sight identification and mitigation for indoor localization using ultra-wideband sensor networks

Thesis (PhD (Computer Engineering))--University of Pretoria, 2020.With the advent of Industry 4.0, indoor localization is central to many applications across multiple domains. Although impulse-radio ultra-wideband (IR-UWB) enables high precision time-of-arrival (TOA) based ranging and localization for wireless sensor networks, there are several challenges, including multi-user interference and non-line-of-sight (NLOS) conditions. NLOS conditions occur when the communication path between receiver and transmitter is obstructed, and these conditions are frequent indoors due to walls and other obstructions. To maintain location accuracy and precision similar to line-of-sight (LOS) conditions, identification and mitigation of these NLOS conditions is crucial. For identification and mitigation methods to be implemented in sensor networks, they must be of low complexity to minimize their influence on localization requirements. This thesis investigates NLOS identification and mitigation for IEEE 802.15.4a IR-UWB sensor networks. The objective of this thesis is to improve location accuracy in NLOS conditions for IR-UWB sensor networks. A comprehensive review of the state-of-the-art in NLOS identification and mitigation is conducted, and limitations of these methods with regards to the use of multiple channels, dependence on training data, mobility and complexity (particularly for applications with time constraints) are highlighted. This thesis proposes identification and mitigation methods that address the limitations found in state-of-the-art methods. A distance residual-based method for NLOS identification is proposed. Compared to conventional NLOS identification which relies on knowledge of LOS and NLOS channel statistics, or analysis of the standard deviation of range measurements over time, this identification method does not rely on these parameters. A NLOS classification method that distinguishes between through-the-wall and around-the-corner conditions using channel statistics extracted from channel impulse responses is proposed. Unlike most methods in literature that focus on distinguishing between LOS and NLOS, this method classifies NLOS conditions into through-the-wall and around-the-corner, therefore providing more context to the location estimate, and consequently enabling mitigation methods to be used for specific types of NLOS conditions. A through-the-wall ranging error mitigation method that relies on floor plans is proposed. A novel model for through-the-wall TOA ranging is proposed and experimentally evaluated. The conventional throughthe- wall TOA ranging model in literature requires many parameters which cannot be calculated in realistic scenarios. Compared to through-the-wall TOA ranging models found in literature, the proposed model relies on information from floor plans to reduce the number of unknown parameters in the model. The results show that NLOS errors caused by through-the-wall propagation are significantly mitigated with the proposed method, resulting in location accuracy which approaches the LOS case. A NLOS mitigation method which corrects location estimates affected by random ranging errors is proposed. This method relies on geometric constraints based on the fact that biases introduced by NLOS conditions in TOA range measurements are positive. The method is evaluated for cases where NLOS ranges are identifiable and cases where they are not identifiable. For the latter case, the results show that the proposed method significantly outperforms state-of-the-art optimization-based mitigation methods in terms of execution time, while retaining similar performance in terms of location accuracy.Electrical, Electronic and Computer EngineeringPhD (Computer Engineering)UnrestrictedFaculty of Engineering, Built Environment and Information TechnologySDG-09: Industry, innovation and infrastructureSDG-11:Sustainable cities and communitie