Examinando por Autor "Magsi, Amjad Hussain"

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  • Miniatura
    Ítem
    Comparison of FGO and KF for PDR-GNSS fusion under different PDR errors
    (Institute of Electrical and Electronics Engineers Inc., 2024) Magsi, Amjad Hussain; Díez Blanco, Luis Enrique
    Smartphone-based positioning, heralded for its widespread accessibility, encounters challenges due to its reliance on global navigation satellite systems (GNSSs) in unfavorable conditions such as urban canyons, tunnels, and indoor areas. Even in clear-sky conditions, signal distortions, interruptions, and the limitations of cost-effective smartphone GNSS prompt researchers to explore alternative positioning methods. This has led to the adoption of sensor fusion techniques, often integrating the inertial measurement unit (IMU) for its complementary features with GNSS. In pedestrian localization, the fusion of pedestrian dead reckoning (PDR) and GNSS, traditionally employing the Kalman filter (KF) as the main fusion algorithm, has been standard practice. The emerging factor graph optimization (FGO) algorithm has recently gained attention for its better accuracy for inertial navigation system (INS)-GNSS fusion architectures, especially under GNSS outliers. However, the FGO implementation for PDR-GNSS fusion architecture has been less investigated, and little is known about its performance under different PDR outliers. As the different gait dynamics of humans and transient variations in the way the smartphone is carried, the PDR system can generate short and high errors (SHEs) or continuous and low errors (CLEs). We analyze the improvement of FGO over KF in mitigating these PDR errors in the PDR-GNSS fusion architectures for smartphone-based positioning. Since FGO is a smoothing technique and KF is a filtering method, for a fairer comparison, we also implemented a smoothed KF (SKF) using the Rauch-Tung-Striebel smoother (RTSS) technique. Our investigation, involving ten individuals with diverse heights, genders, and gait patterns in walking and running motions, underscores FGO's superior performance in the presence of PDR errors and across various pedestrian and motion scenarios, achieving a stable 25% improvement for the mean position error and 30% for the median position error in comparison to KF, 24% mean improvement, and 32% median improvement in comparison to SKF. Furthermore, the convergence time for FGO after the SHE PDR errors is comparably shorter than SKF and KF.
  • Miniatura
    Ítem
    Continuous high-precision positioning in smartphones by FGO-based fusion of GNSS–PPK and PDR
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024-09) Magsi, Amjad Hussain; Díez Blanco, Luis Enrique; Knauth, Stefan
    The availability of raw Global Navigation Satellites System (GNSS) measurements in Android smartphones fosters advancements in high-precision positioning for mass-market devices. However, challenges like inconsistent pseudo-range and carrier phase observations, limited dual-frequency data integrity, and unidentified hardware biases on the receiver side prevent the ambiguity resolution of smartphone GNSS. Consequently, relying solely on GNSS for high-precision positioning may result in frequent cycle slips in complex conditions such as deep urban canyons, underpasses, forests, and indoor areas due to non-line-of-sight (NLOS) and multipath conditions. Inertial/GNSS fusion is the traditional common solution to tackle these challenges because of their complementary capabilities. For pedestrians and smartphones with low-cost inertial sensors, the usual architecture is Pedestrian Dead Reckoning (PDR)+ GNSS. In addition to this, different GNSS processing techniques like Precise Point Positioning (PPP) and Real-Time Kinematic (RTK) have also been integrated with INS. However, integration with PDR has been limited and only with Kalman Filter (KF) and its variants being the main fusion techniques. Recently, Factor Graph Optimization (FGO) has started to be used as a fusion technique due to its superior accuracy. To the best of our knowledge, on the one hand, no work has tested the fusion of GNSS Post-Processed Kinematics (PPK) and PDR on smartphones. And, on the other hand, the works that have evaluated the fusion of GNSS and PDR employing FGO have always performed it using the GNSS Single-Point Positioning (SPP) technique. Therefore, this work aims to combine the use of the GNSS PPK technique and the FGO fusion technique to evaluate the improvement in accuracy that can be obtained on a smartphone compared with the usual GNSS SPP and KF fusion strategies. We improved the Google Pixel 4 smartphone GNSS using Post-Processed Kinematics (PPK) with the open-source RTKLIB 2.4.3 software, then fused it with PDR via KF and FGO for comparison in offline mode. Our findings indicate that FGO-based PDR+GNSS–PPK improves accuracy by 22.5% compared with FGO-based PDR+GNSS–SPP, which shows smartphones obtain high-precision positioning with the implementation of GNSS–PPK via FGO.