In complex indoor environments, achieving satisfactory pedestrian localization using a single positioning technique proves challenging. For instance, Ultra-Wideband (UWB) positioning encounters non-line-of-sight errors in intricate indoor settings, while Pedestrian Dead Reckoning (PDR) technology with inertial sensors is susceptible to cumulative drift errors over time. Consequently, this paper introduces the Unscented Kalman Filter (UKF) algorithm to integrate UWB technology with PDR technology. The improved PDR-derived pedestrian localization information is employed as the state vector for the UKF algorithm, and the positioning information obtained through enhanced UWB technology serves as the observation vector for the UKF algorithm. This combined approach effectively corrects pedestrian position information, ultimately yielding more accurate pedestrian locations. Research results indicate that the proposed algorithm achieves a root mean square error of 3.64 centimeters. In comparison to a standalone UWB algorithm, this method demonstrates superior positioning accuracy in complex environments.
For pedestrian indoor positioning in a complex environment, a single positioning technology cannot achieve a good positioning effect. For example, ultra wide band (UWB) positioning technology cannot reduce the influence of non-line-of-sight (NLOS) error by itself. Therefore, this paper uses extended Kalman filter (EKF) algorithm to integrate UWB technology with pedestrian dead reckoning (PDR) technology. PDR positioning technology has the advantages of strong autonomy and high short-time positioning accuracy, which is complementary to UWB positioning technology. In order to facilitate the fusion of UWB and PDR and facilitate the use of pedestrian, the PDR device was fixed on the waist of pedestrian, and a gait detection method based on multiple constraints was proposed for pedestrian gait recognition. Aiming at the problem of NLOS error of UWB in the process of fusion, an adaptive noise variance method is proposed to dynamically adjust the measurement covariance of UWB. Through experimental verification, the average error of the positioning results of the proposed method is 0.21m, which is significantly more accurate than that of pure UWB positioning or pure PDR positioning.
In the field of the mobile robot indoor location, aiming at the problems of poor stability of the WiFi signal fingerprint location and low accuracy of the single sensor location, this paper proposes a multi-sensor fusion assisted WiFi signal fingerprint location method for a mobile robot. This method is based on the extended Kalman filter (EKF) algorithm, combined with the trajectory information obtained from the inertial measurement unit (IMU) and the odometer, to fuse and correct the WiFi signal fingerprint positioning results, so as to realize a fusion positioning method with WiFi positioning as the main and multi-sensor positioning as the auxiliary. The experimental results show that the average positioning error of the fusion positioning algorithm proposed in this paper is controlled at 0.98 m, which can effectively solve the problem that fingerprint positioning using WiFi signal is greatly disturbed by the environment, and avoid the cumulative error caused by dead reckoning (DR), and improve the robustness and positioning accuracy of the positioning system.
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