Browsing by Author "Xu, Shuoyuan"

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    Distributed estimation over a low-cost sensor network: a review of state-of-the-art
    (Elsevier, 2019-06-23) He, Shaoming; Shin, Hyosang; Xu, Shuoyuan; Tsourdos, Antonios
    Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted.
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    Experimental evaluation of GNSS and IMU fusion using gated recurrent unit
    (IEEE, 2022-05-12) Xu, Shuoyuan; Petrunin, Ivan; Tsourdos, Antonios
    In this paper, a data-driven Inertial navigation systems (INS) and Global Navigation Satellite System (GNSS) fusion algorithm based on the use of the Gated Recur-rent Unit (GRU) is proposed. In this project, we trained the GRU neural network with Inertial Measurement Unit (IMU) raw data and GNSS Position, Velocity and Timing (PVT) solutions as input and the position difference between GNSS and ground truth as labels. Therefore, the trained model can estimate the rover’s positions by subtracting the predicted GNSS error from GNSS positions given IMU raw measurements and GNSS PVT solutions. To evaluate the performance of GNSS/INS fusion algorithms in realistic scenarios, we developed an experimental platform. Our experimental platform consists of a moving test rig and an external validation system. The moving test rig consists of a rover equipped with an LPMS-CU2: 9-Axis Inertial Measurement Unit (IMU) and U-Blox ZED-F9P GNSS receiver. For validation purposes, we employ an onboard real-time kinematic positioning (RTK)-GNSS receiver. The test scenarios include both open-sky and challenging conditions near buildings, which is beneficial for devolving and testing urban navigation systems. After training with collected experimental data in multiple test scenarios, the proposed algorithm is able to improve GNSS positioning accuracy by more than 60% for the open-sky environment and 30% for the urban environment.
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    Real-time implementation of YOLO+JPDA for small scale UAV multiple object tracking
    (IEEE, 2018-09-03) Xu, Shuoyuan; Savvaris, Al; He, Shaoming; Shin, Hyosang; Tsourdos, Antonios
    This paper describes the development of a real-time multiple object detection and tracking system for a small scale UAV. The YOLO deep learning visual object detection algorithm and JPDA multiple target detection algorithm, were selected and implemented. The theory and implementation details of these algorithms are presented. The performance analysis of the system is done on both public dataset and aerial videos taken by UAV.