Human Body Shadowing Effect on UWB-Based Ranging System for Pedestrian Tracking

Tian, Qinglin; Wang, Kevin I‐Kai; Salčić, Zoran

doi:10.1109/tim.2018.2884605

Cited by 66 publications

(56 citation statements)

References 24 publications

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“…Additional works in [14]- [18] have gone a step further to model the NLOS error created by body shadowing in to probability density functions (PDFs) such as Gamma by [14] and [15] for a sensor that is handheld, Gaussian by [16]- [18] for a sensor that is mounted on the wrist and chest, respectively. However, the main drawback of these works is that the proposed models: 1) Have been performed for TAG mounted positions that can easily obtain sub-meter level accuracy without any body shadowing mitigation.…”

Section: Figurementioning

confidence: 99%

Towards Sub-Meter Level UWB Indoor Localization Using Body Wearable Sensors

et al. 2020

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Thanks to its ability to provide sub-meter level positioning accuracy, Ultrawideband (UWB) has found wide use in several wireless body area network (WBAN) applications such as ambient assisted living, remote patient management and preventive care, among others. In spite of the attractiveness of UWB, it is not possible to achieve this level of accuracy when the human body obstructs the wireless channel, leading to a bias in the Time of Flight (TOF) measurements, and hence a detection of position errors of several meters. In this paper, a study of how a sub-meter level of accuracy can be achieved after compensating for body shadowing is presented. Using a Particle Filter (PF), we apply UWB ranging error models that take into consideration the body shadowing effect and evaluate them through simulations and extensive measurements. The results show a significant reduction in the median position error of up to 75 % and 82 % for simulations and experiments, respectively, leading to the achievement of a sub-meter level of localization accuracy. INDEX TERMS Body wearable sensors, Human body shadowing, Localization, Ranging, Ultrawideband, Time of Flight, Particle Filter I. INTRODUCTION With rapid developments of computer and miniaturization technologies, wearable sensors are becoming an important part of our daily lives. With a market expected to grow to $ 70 billion in 2025 [1], wearables are widely used in several applications such as in sport science, rehabilitation, medical monitoring, surveillance, among others [2]-[4]. Therefore, they can be attached to shoes, eyeglasses, earrings, clothing, gloves and watches, etc of the user of the wearable. Collecting precise user localization information is one of the salient capabilities of wearables [5]. In fact, according to [6], a sub-meter level of localization accuracy is envisaged for several wireless body area networks (WBAN). Currently, Global Navigation Satellite System (GNSS) is the most widely spread localization technology in outdoor environments.

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Section: Figurementioning

confidence: 99%

Towards Sub-Meter Level UWB Indoor Localization Using Body Wearable Sensors

et al. 2020

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“…In this work, the wearable sensor is placed on the waist of the body. Tian et al [29] studied the effect of human body shadowing on the ranging and positioning results, however, the results are only for a handheld sensor. Jie He et al [30] analyzed the TOF ranging error along a straight corridor, but for wearables mounted on the chest and wrist positions.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, in [37], we developed ranging error models for different body wearable sensor positions such as the forehead, hand, chest, wrist, arm, thigh and ankle. Other ranging error models are also presented for the hand [29,32], for the wrist and chest [30,38,39], and for the waist position [28].…”

Section: Related Workmentioning

confidence: 99%

Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy

et al. 2019

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Over the years, several Ultrawideband (UWB) localization systems have been proposed and evaluated for accurate estimation of the position for pedestrians. However, most of them are evaluated for a particular wearable sensor position; hence, the accuracy obtained is subject to a given wearable sensor position. This paper is focused on studying the effects of body wearable sensor positions i.e., chest, arm, ankle, wrist, thigh, forehead, and hand, on the localization accuracy. According to our results, the forehead and the chest provide the best and worst body sensor location for tracking a pedestrian, respectively. With the wearable sensor at the forehead and chest position, errors lower than 0.35 m (90th percentile) and 4 m can be obtained, respectively. The reason for such a contrast in the performance lies in the fact that, in non-line-of-sight (NLOS) situations, the chest generates the highest multipath of any part of the human body. Thus, the large errors obtained arise due to the signal arriving at the target wearable sensor by multiple reflections from interacting objects in the environment rather than by direct line-of-sight (LOS) or creeping wave propagation mechanism.

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“…It is observed that the heading orientation obtained from gyroscope is stable when the pedestrian is walking in a straight line and the drift accumulates when a turn is made. In this paper, a turn detection-based gyroscope drift correction approach is proposed and incorporated with an INS and UWB FIPS, where only distance measurements from UWB subsystem are used [19]. The contributions of the paper are listed as follows:…”

Section: Introductionmentioning

confidence: 99%

An INS and UWB Fusion-Based Gyroscope Drift Correction Approach for Indoor Pedestrian Tracking

Tian

Wang

Salčić

2020

Sensors

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Information fusion combining inertial navigation and radio frequency (RF) technologies, is commonly applied in indoor positioning systems (IPSs) to obtain more accurate tracking results. The performance of the inertial navigation system (INS) subsystem is affected by sensor drift over time and the RF-based subsystem aims to correct the position estimate using a fusion filter. However, the inherent sensor drift is usually not corrected during fusion, which leads to increasingly erroneous estimates over a short period of time. Among the inertial sensor drifts, gyroscope drift has the most significant impact in determining the correct orientation and accurate tracking. A gyroscope drift correction approach is proposed in this study and is incorporated in an INS and ultra-wideband (UWB) fusion IPS where only distance measurements from UWB subsystem are used. The drift correction approach is based on turn detection to account for the fact that gyroscope drift is accumulated during a turn. Practical pedestrian tracking experiments are conducted to demonstrate the accuracy of the drift correction approach. With the gyroscope drift corrected, the fusion IPS is able to provide more accurate tracking performance and achieve up to 64.52% mean position error reduction when compared to the INS only tracking result.

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Human Body Shadowing Effect on UWB-Based Ranging System for Pedestrian Tracking

Cited by 66 publications

References 24 publications

Towards Sub-Meter Level UWB Indoor Localization Using Body Wearable Sensors

Towards Sub-Meter Level UWB Indoor Localization Using Body Wearable Sensors

Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy

An INS and UWB Fusion-Based Gyroscope Drift Correction Approach for Indoor Pedestrian Tracking

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