An Improved UWB/IMU Tightly Coupled Positioning Algorithm Study

Abstract

The combination of ultra-wide band (UWB) and inertial measurement unit (IMU) positioning is subject to random errors and non-line-of-sight errors, and in this paper, an improved positioning strategy is proposed to address this problem. The Kalman filter (KF) is used to pre-process the original UWB measurements, suppressing the effect of range mutation values of UWB on combined positioning, and the extended Kalman filter (EKF) is used to fuse the UWB measurements with the IMU measurements, with the difference between the two measurements used as the measurement information. The non-line-of-sight (NLOS) measurement information is also used. The optimal estimate is obtained by adjusting the system measurement noise covariance matrix in real time, according to the judgment result, and suppressing the interference of non-line-of-sight factors. The optimal estimate of the current state is fed back to the UWB range value in the next state, and the range value is dynamically adjusted after one-dimensional filtering pre-processing. Compared with conventional tightly coupled positioning, the positioning accuracy of the method in this paper is improved by 46.15% in the field experimental positioning results.

Research on Positioning and Simulation Method for Autonomous Mobile Construction Platform

“…In the mobile state, the positioning errors in the x and y directions are 3.9 cm and 5.2 cm, and the trajectory error is 8 cm. Compared with other studies, which have errors of 4.1 cm and 5.8 cm in the x and y directions [58], and trajectory errors of 28 cm and 10 cm [60,61], the method in this paper shows a high level of positioning accuracy.…”

“…Multi-sensor fusion can compensate for the shortcomings of a single positioning system, so multi-sensor fusion positioning is better than single sensor positioning in terms of positioning accuracy and stability. Because IMU is not affected by the external environment, it can achieve positioning only by itself, which can effectively overcome the random error in the process of UWB positioning, and the UWB positioning system can make up for the long-term drift error generated in the process of IMU positioning [58]. In the process of multi-sensor fusion positioning, KF, a specific implementation of Bayesian filtering, is widely used in the state estimation problem.…”

“…ment, it can achieve positioning only by itself, which can effectively overcome the random error in the process of UWB positioning, and the UWB positioning system can make up for the long-term drift error generated in the process of IMU positioning [58]. In the process of multi-sensor fusion positioning, KF, a specific implementation of Bayesian filtering, is widely used in the state estimation problem.…”

A Combined UWB/IMU Localization Method with Improved CKF

“…In the NLOS case, the ability to deal with nonlinear noise through the KF is limited, and the localization results are not good. Zou et al [16] preprocessed the original UWB measurements using a KF to suppress the UWB distance mutation values and fused the UWB and IMU measurements using an extended Kalman filter (EKF) to adjust the system's measurement noise covariance matrix in real time, which suppressed the interference of the NLOS effect to a certain extent and improved the localization performance. Narasimhappa et al [17] designed an improved Sage-Husa adaptive KF, a first-order autoregressive model was used to model the random error of the gyroscope, and the model was used to initialize the transition matrix of the Sage-Husa adaptive KF, and finally, simulations were performed to validate the filter's performance, which was superior to the KF in removing noise.…”