Abstract
Ultra wideband (UWB) has been a popular technology for indoor positioning due to its high accuracy. However, in many indoor application scenarios UWB measurements are influenced by outliers under non-line of sight (NLOS) conditions. To detect and eliminate outlying UWB observations, we propose a UWB/Inertial Measurement Unit (UWB/IMU) fusion filter based on a Complementary Kalman Filter to track the errors of position, velocity and direction. By using the least squares method, the positioning residual of the UWB observation is calculated, the robustness factor of the observation is determined, and an observation weight is dynamically set. When the robustness factor does not exceed a pre-defined threshold, the observed value is considered trusted, and adaptive filtering is used to track the system state, while the abnormity of system state, which might be caused by IMU data exceptions or unreasonable noise settings, is detected by using Mahalanobis distance from the observation to the prior distribution. When the robustness factor exceeds the threshold, the observed value is considered abnormal, and robust filtering is used, whereby the impact of UWB data exceptions on the positioning results is reduced by exploiting Mahalanobis distance. Experimental results show that the observation error can be effectively estimated, and the proposed algorithm can achieve an improved positioning accuracy when affected by outlying system states of different quantity as well as outlying observations of different proportion.
Highlights
Indoor positioning technology is important in a variety of applications, ranging from supermarket shopping to drone positioning and hospital patient tracking [1,2,3]
Xu et al [10] developed a new approach using least squares support vector machine and H∞ filter for IMU/wireless sensor network (WSN) integration and achieved a reduction of positioning error by 14.8% compared with the ultra wideband (UWB)-only model
We propose a method to minimize the effect of outliers in UWB/Inertial Measurement Unit (UWB/IMU) fusion
Summary
Indoor positioning technology is important in a variety of applications, ranging from supermarket shopping to drone positioning and hospital patient tracking [1,2,3]. The observations are obtained from the difference between the UWB ranging and the distance from the beacon to the position obtained by IMU integration at 2 Hz. Each time the system state is updated, the position, velocity and direction errors contained in it are directly fed back to the navigation equation to calculate the result of the error correction, and the bias of the acceleration and the gyroscope are used to correct the original value of the accelerometer and the gyroscope, respectively. In order to suppress the influence of abnormal system state and observations on positioning accuracy, an Adaptive-Robust filtering method based on Mahalanobis distance and robustness factor is designed in this paper.
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