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. 2025 Mar 18;25(6):1883.
doi: 10.3390/s25061883.

Adaptive Distributed Student's T Extended Kalman Filter Employing Allan Variance for UWB Localization

Yanli Gao  1   2   3 , Maosheng Yang  4   5 , Xin Zang  4   5 , Lei Deng  6 , Manman Li  6 , Yuan Xu  3   4   5 , Mingxu Sun  4
Affiliations

Adaptive Distributed Student's T Extended Kalman Filter Employing Allan Variance for UWB Localization

Yanli Gao et al. Sensors (Basel). .

Abstract

This study proposes an adaptive distributed Student's t extended Kalman filter (EKF) using Allan variance for ultrawide-band (UWB) localization. First of all, we model the state equation using the target's position and velocity in east and north directions and the measurement equation by using distance between the UWB base station (BS) and the target object. Then, the adaptive distributed filter employs a federation structure: A local t EKF is designed to estimate the target's position by fusing the distance between the UWB base station and the target object. The main filter fuses the local filter's outputs and computes the final output. For the local t EKF, in order to overcome the problem that noise in the Kalman method is assumed to be white noise and difficult to adapt to practical application environments, the t distribution is used to model noise. Meanwhile, Allan variance is calculated to assist the local filter, which improves the adaptive ability. Experimental results show that the proposed method effectively enhances navigation accuracy compared to the distributed EKF.

Keywords: UWB; extended Kalman filter; t distribution.

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Conflict of interest statement

Authors Yanli Gao and Yuan Xu were employed by Shandong Huichuang Information Technology Co., Ltd. Authors Maosheng Yang, Xin Zang and Yuan Xu were employed by Jinan Chenhe Information Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The structure of this paper.
Figure 2
Figure 2
The structure of the UWB localization with the adaptive distributed student’s t extended Kalman filter with Allan variance.
Figure 3
Figure 3
Environment for UWB mobile robot localization.
Figure 4
Figure 4
The mobile robot used in the test.
Figure 5
Figure 5
The planned path, path of UWB measurements, and paths measured by the federal EKF, distributed t EKF, and adaptive distributed t EKF with Allan variance for the mobile robot localization.
Figure 6
Figure 6
The east absolute position error measured by the UWB, federal EKF, distributed t EKF, and adaptive distributed t EKF with Allan variance for the mobile robot localization.
Figure 7
Figure 7
The north absolute position error of the UWB measurements, federal EKF, distributed t EKF, and adaptive distributed t EKF with Allan variance for the mobile robot localization.
Figure 8
Figure 8
Environment for UWB robotic dog localization.
Figure 9
Figure 9
The robotic dog used in the test.
Figure 10
Figure 10
The planned path used by the robotic dog in the test.
Figure 11
Figure 11
The planned path and the paths of the UWB measurements, federal EKF, distributed t EKF, and adaptive distributed t EKF with Allan variance for the robotic dog localization.
Figure 12
Figure 12
The eastward absolute position error of the UWB measurements, federal EKF, distributed t EKF, and adaptive distributed t EKF with Allan variance for the robotic dog localization.
Figure 13
Figure 13
The northward absolute position error measured by the UWB, federal EKF, distributed t EKF, and adaptive distributed t EKF with Allan variance for the robotic dog localization.
See this image and copyright information in PMC

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