## 1 Introduction  
The automobile industry is the largest manufacturing industry in the world with revenues of over 1 trillion US dollars and more than 10 million employees handling roughly 20,000 components in complex assembly processes. The automobile assembly process is labor intensive, in which approximately 3000 units of interior material, dashboard, seat, windshield, engine, transmission, and axle are assembled in a painted automobile body, thus having the lowest automation rate compared to other processes [1].  
The current automobile assembly process is not monitored in real time, which causes difficulty in identifying a missing operation in each process. Therefore, if the location of the tool currently being worked on can be determined, it can improve the productivity and quality in the manufacturing process by identifying missing or redundant tasks to prevent manufacturing detects [2, 3].  
Herein, we propose a position tracking system of a tool using a UWB (ultra-wide band) and a MEMS (microelectro-mechanical system) IMU (inertial measurement unit) sensor for realtime monitoring of the position of tools. Recently, extensive research has been conducted on real-time position recognition systems using the UWB technology in which Wi-Fi uses a bandwidth of approximately 20 MHz, whereas UWB uses a wide bandwidth of several gigahertz; hence, UWB requires ten times less power than Wi-Fi but has a short transmission distance of 10 m [4, 5]. Furthermore, an IMU is a device for measuring the speed, direction, gravitational force, and acceleration of a moving object by using an accelerometer, an angular velocity meter, a geomagnetic meter, and an altimeter, respectively [6].  
We have previously studied the Kalman filter-based positioning system using MEMS IMU and UWB sensors [7]. However, any work tools are positioned out of 200 mm because of the linear system of the Kalman filter. This system should be improved by an advanced Kalman filter, such as the extended Kalman filter, complementary Kalman filter and the accurate TWR system, to prevent the distance by obstacles in the indoor environment. In this study, a UWB communication module was used to measure the position of a work tool, and the MEMS IMU sensor was used to provide positioning calibration data for reducing the measurement error of the UWB. The output data of the IMU sensor or UWB contain noises in addition to positioning data because of electrical or mechanical reasons.  
Our study has the following features about UWB and MEMS IMU integrated positioning algorithm for accurate work-tool tracking. Firstly, the accurate position, velocity, and attitude are measured through the MEMS IMU sensor derived from the inertial navigation formula in 3 coordinate frames of indoor positioning frame, local navigation frame, EGI frame. Secondly, the distance between an anchor node and a tag node by the TWR method is computed by the confidence-distance-compensated trilateration localization method to prevent the distance error by obstacles or changes in the indoor environment. Thirdly, the improved EKR (extended Kalman filter) by the error dynamic equation is derived from 8 \(\times\) 8 sparse state-space matrix used for the integrated positioning algorithms of MEMS IMU and UWB sensors.  
Any work tools that have deviated from the set operation path can be identified in real time using the position tracking system of a tool proposed in this study, thus enabling any omitted operations to be identified; the positioning accuracy was found to be 200 mm or less through multiple experiments, which indicates that the location of the tools can be accurately identified when the operation points are spaced more than 200 mm apart.  
In Section 2, the proposed position tracking system based on a composite sensor is introduced. In Section 3, the validity of the proposed position tracking system was verified through the basic unit testing results. In Section 4, the experiment results for evaluating the performance of the proposed position tracking system are presented.