• Extending Reliability of mmWave Radar Tracking and Detection via Fusion With Camera

      Zhang, Renyuan; Cao, Siyang; Univ Arizona, Dept Elect & Comp Engn (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019)
      In this paper, a new radar-camera fusion system is presented. The fusion system takes into consideration the error bounds of the two different coordinate systems from the heterogeneous sensors, and further a new fusion-extended Kalman filter is utilized to adapt to the heterogeneous sensors. Real-world application considerations such as asynchronous sensors, multi-target tracking and association are also studied and illustrated in this paper. Experimental results demonstrated that the proposed fusion system can realize a range accuracy of 0.29m with an angular accuracy of 0.013rad in real-time. Therefore, the proposed fusion system is effective, reliable and computationally efficient for real-time kinematic fusion applications.
    • Extending Reliability of mmWave Radar Tracking and Detection via Fusion With Camera

      Zhang, Renyuan; Cao, Siyang; Univ Arizona, Dept Elect & Comp Engn (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019-09-19)
      In this paper, a new radar-camera fusion system is presented. The fusion system takes into consideration the error bounds of the two different coordinate systems from the heterogeneous sensors, and further a new fusion-extended Kalman filter is utilized to adapt to the heterogeneous sensors. Real-world application considerations such as asynchronous sensors, multi-target tracking and association are also studied and illustrated in this paper. Experimental results demonstrated that the proposed fusion system can realize a range accuracy of 0.29m with an angular accuracy of 0.013rad in real-time. Therefore, the proposed fusion system is effective, reliable and computationally efficient for real-time kinematic fusion applications.
    • Model-Based Heterogeneous Data Fusion for Reliable Force Estimation in Dynamic Structures under Uncertainties

      Khodabandeloo, Babak; Melvin, Dyan; Jo, Hongki; Univ Arizona, Civil Engn & Engn Mech (MDPI AG, 2017-11-17)
      Direct measurements of external forces acting on a structure are infeasible in many cases. The Augmented Kalman Filter (AKF) has several attractive features that can be utilized to solve the inverse problem of identifying applied forces, as it requires the dynamic model and the measured responses of structure at only a few locations. But, the AKF intrinsically suffers from numerical instabilities when accelerations, which are the most common response measurements in structural dynamics, are the only measured responses. Although displacement measurements can be used to overcome the instability issue, the absolute displacement measurements are challenging and expensive for full-scale dynamic structures. In this paper, a reliable model-based data fusion approach to reconstruct dynamic forces applied to structures using heterogeneous structural measurements (i.e., strains and accelerations) in combination with AKF is investigated. The way of incorporating multi-sensor measurements in the AKF is formulated. Then the formulation is implemented and validated through numerical examples considering possible uncertainties in numerical modeling and sensor measurement. A planar truss example was chosen to clearly explain the formulation, while the method and formulation are applicable to other structures as well.