Browsing by Author "Haque, Fahimul"
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- ItemEmbargoClustering-Assisted Observation Domain Optimization for GNSS Multi-Fault Detection and Mitigation(2024-07-15) Haque, Fahimul; Dehghanian, Vahid; Fapojuwo, Abraham; Dehghanian, Vahid; Fapojuwo, Abraham; Nielsen, Jorgen; O'Keefe, Kyle; Messier, Geoffrey; Alves, PauloWith the rise of autonomous and semi-autonomous vehicles, effective fault detection and mitigation (FDM) methods have become essential in meeting the integrity requirements for precise and reliable Global Navigation Satellite System (GNSS)-based positioning. In scenarios involving multiple faulty observations, the existing GNSS-only statistical FDM methods are ineffective or impractical due to either theoretical model limitations or high computational costs. Additionally, supervised learning-based FDM approaches introduced in recent years do not meet the existing and emerging industry requirements due to dependence on large amounts of diverse training data, the accuracy of the offline labeling process, or high computational complexity. In this dissertation, a novel GNSS multi-fault detection and mitigation method is developed that achieves a balance between computational complexity and performance. The proposed method incorporates an Expectation Maximization (EM) framework to jointly estimate an approximate maximum likelihood of states and latent model parameters in the presence of observation outliers, i.e., faults. However, the EM algorithm is known for its high computational complexity. To reduce the computational complexity of EM, an importance sampling step based on unsupervised clustering is introduced. As demonstrated by the results and analysis herein, the proposed method outperforms the existing Least-squares Residuals-based single-fault method, achieving an average improvement of up to 48% in positioning accuracy. Additionally, the computational complexity of the proposed method is an order of magnitude lower than the state-of-the-art Solution Separation method. The improved performance and the lower computational complexity of the proposed method make it a suitable candidate for integration into modern standalone real-time GNSS applications.
- ItemOpen AccessSensor Fusion-based Framework for Floor Localization(2018-07-03) Haque, Fahimul; Dehghanian, Vahid; Fapojuwo, Abraham Olatunji; Nielsen, Jørgen S.; Messier, Geoffrey G.Floor localization is at the heart of indoor positioning systems (IPSs) in multi-storey buildings with a variety of commercial, industrial, and health and safety applications. The prevalence of wireless technologies along with the integration of micro electro-mechanical sensors (e.g. barometers) in handheld devices and wearable gadgets of current vintage have prompted a surge in research and development efforts in the IPS area. Received signal strength (RSS), barometric altimetry (BA), and differential barometric altimetry (DBA) are three well-known methods of floor localization. However, the RSS-based methods lack the required accuracy, BA-based methods are prone to random errors due to local changes in the air pressure, e.g. from approaching weather systems, and DBA-based methods require installation of additional infrastructure (e.g. reference nodes and ad-hoc network for real-time information exchange). Fusion of BA and RSS is a viable solution for floor localization; nevertheless, available fusion algorithms are rather heuristic. In this dissertation, a theoretical framework is developed for fusing BA and Wi-Fi RSS measurements. The proposed framework involves a novel Monte Carlo Bayesian inference algorithm, for processing RSS measurements, and then fusion with BA using a Kalman Filter scheme. As demonstrated by our experimental results, the proposed sensor fusion algorithm achieves floor localization accuracy of 97% on average. The algorithm does not require new infrastructure, and has low computational complexity, hence, can be readily integrated into various state-of-the-art mobile devices.