Robust IMU-Based Mitigation of Human Body Shadowing in UWB Indoor Positioning

Abstract

Ultra-wideband (UWB) indoor positioning systems have the potential to achieve sub-decimeter-level accuracy. However, the ranging performance degrades significantly under non-line-of-sight (NLoS) conditions. The detection and mitigation of NLoS conditions is a complex problem and has been the subject of many works over the past decades. When localizing pedestrians, human body shadowing (HBS) is a particular and specific cause of NLoS. In this paper, we present an HBS mitigation strategy based on the orientation of the body and tag relative to the UWB anchors. Our HBS mitigation strategy involves a robust range error model that interacts with a tracking algorithm. The model consists of a bank of Gaussian Mixture Models (GMMs), from which an appropriate GMM is selected based on the relative body-tag-anchor orientation. The relative orientation is estimated by means of an inertial measurement unit (IMU) attached to the tag and a candidate position provided by the tracking algorithm. The selected GMM is used as a likelihood function for the tracking algorithm to improve localization accuracy. Our proposed approach was realized for two tracking algorithms. We validated the implemented algorithms on dynamic UWB ranging measurements, which were performed in an industrial lab environment. The proposed algorithms outperform other state-of-the-art algorithms, achieving a 37% reduction of the p75 error.

Keywords: Gaussian mixture model; IMU; UWB; human body shadowing; indoor localization; particle filter.

Figure 1

Visualization of angles and vectors related to estimation of the relative body orientation.

Figure 2

Part of the measurement area of the IIoT lab (a) and the on-body setup carried by the user (b).

Figure 3

Ground truth trajectories, surrounded by a red dashed rectangle marking the effective measurement area. One trajectory (a) consists almost entirely of smooth turns, while the other (b) consists of straight parts and sharp turns. The dots in the corners represent the UWB anchors. Black lines represent concrete walls to which the anchors are attached.

Figure 4

Flowchart of the human body shadowing mitigation approach. The blue blocks represent the hardware measurements and known UWB anchor locations. The green blocks represent the processes of a typical tracking algorithm. The red (encircled with dashes) and yellow blocks represent the offline and online part of the proposed mitigation method, respectively. The arrows show how the output of each block serves as input to one or more other blocks.

Figure 5

Range error statistics as a function of the body–tag–anchor orientation $\varphi \in [0^{\circ },{180}^{\circ }]$.

Figure 6

Histograms (blue) of UWB range error subsets with fitted GMMs (red). Each subset is sampled around $\varphi$ with a Gaussian window from the IIoT lab static experiment dataset discussed in [29]. (a–d) show how the amount of Gaussian components K needed to train the model, is proportional to $\varphi$.

Figure 7

CDFs of localization errors of proposed and referenced algorithms.

Figure 8

Plots of the estimated (red) over ground truth (blue) 2D trajectories for the EKF (a,e) algorithm, the reference [27] (b,f), the proposed filter algorithms (c,g), and the proposed smoother algorithm (d,h) with a delay of four UWB measurements.

Figure 9

Average position errors of proposed and referenced algorithms as a function of the amount of particles N (a) and of the fixed lag L in seconds (b).

Figure 10

CDFs of the position errors (a) and $\widehat{\varphi }$ errors (b) of the proposed, referenced, and mixed PF algorithms.

Figure 11

Relative runtime of proposed and benchmark algorithms on a logarithmic scale. One hundred percent equals 98.1 s on a Raspberry Pi (RPi) 4 model B running Python 3.11 for an experiment duration of 53 s.