Abstract
The study aims to improve the accuracy and stability of indoor positioning by integrating information from two types of sensors: UWB (Ultra-Wideband) and IMU (Inertial Measurement Unit) calibrated to eliminate system errors. The fusion algorithm utilizes Extended Kalman Filter (EKF) to combine UWB data and IMU measurements. First, the IMU undergoes self-calibration to correct system errors. Then, UWB ranging technology is used to estimate the target's position, and the information from the IMU's gyroscope and accelerometer is used to update the kinematic model. Finally, the EKF filters and predicts measurement errors and system noise to obtain accurate target position estimation. Experimental results demonstrate that this fusion method can enhance the precision and robustness of indoor static target positioning. The research findings are highly applicable in areas such as indoor positioning and intelligent surveillance.
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