Data Fusion of Ultra-Wideband Signals and Inertial Measurement Unit for Real-Time Localization

Date of Award


Degree Name

Ph.D. in Electrical Engineering


Department of Electrical and Computer Engineering


Vamsy Chodavarapu


Autonomous systems usually require accurate localization methods for them to navigate safely in indoor environments. Most localization methods are expensive and difficult to set up. In this work, we built a low-cost and portable indoor location tracking system by using Raspberry Pi 4 computer, ultra-wideband (UWB) sensors, and inertial measurement unit(s) (IMU). We also developed the data logging software and the 2D Kalman filter (KF) sensor fusion algorithm to process the data from a low-power UWB transceiver (Decawave, model DWM1001) module and IMU device (Bosch, model BNO055). Autonomous systems move with different velocities and accelerations, which requires their localization performance to be evaluated under diverse motion conditions. We built a dynamic testing platform to generate not only the ground truth trajectory but also the ground truth acceleration and velocity. In this way, our tracking system's localization performance can be evaluated under dynamic testing conditions. The novel contributions in this work are a low-cost, low-power, tracking system hardware and software design, and a 2D linear stage experimental setup to observe the tracking system's localization performance under different dynamic testing conditions. The 2D testing platform has a 1 m translation length and 80 micrometers of bidirectional repeatability. The tracking system's localization performance is evaluated under dynamic conditions with eight different combinations of acceleration and velocity. The ground truth accelerations varied from 0.6 to 1.6 m/s$^2$ and the ground truth velocities varied from 0.6 to 0.8 m/s. Our experimental results show that the location error can reach up to 50 cm under dynamic testing conditions when only relying on the UWB sensor, with the KF sensor fusion of UWB and IMU, the location error decreases to 13.7 cm. For autonomous systems which require 3D real-time locating service, a 3D tracking device is designed based on the previously mentioned 2D tracking device. The consumer grade IMU BNO055 is replaced by a tactical grade IMU ADIS16495, while the other system parts stay the same. The 3D tracking device requires to run a 3D Kalman filter. The 3D Kalman filter is modified from the 2D Kalman filter by adding one more dimension to its state vector and motion model. An industry standard robotic arm YASKAWA GP7 is selected for generating the 3D ground truth trajectory. The 3D tracking device is attached to the robotic arm to evaluate its 3D localization performance.


UWB, IMU, Localization, 2D Kalman filter, 3D Kalman filter

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