An INS and UWB Fusion Approach With Adaptive Ranging Error Mitigation for Pedestrian Tracking

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

doi:10.1109/jsen.2020.2964287

Cited by 36 publications

(26 citation statements)

References 28 publications

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“…The accuracy of UWB ranging has effects on subsequent position estimation, it is affected by UWB NLoS signal judgement and step stance/still time detection. There are several ways to judge NLoS signal including neural network [32], the power-based way [33] and so on. In this paper, the power-based scheme is selected to find appropriate UWB ranging measurements under NLoS routines.…”

Section: Uwb Ranging Selectionmentioning

confidence: 99%

“…The UWB development kit used in our program is from DecaWave, which can provide QD by reading its register values C, F1, F2, and F3 and calculating according to (15). An empirical judgement standard can be given that the UWB transmission path is in NLoS when QD is greater than 10 dBm and in LoS when QD is less than 6 dBm [33]. All the variables used this subsection is in unit of dBm.…”

Section: Uwb Ranging Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Single UWB Anchor Aided PDR Heading and Step Length Correcting Indoor Localization System

et al. 2021

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Section: Uwb Ranging Selectionmentioning

confidence: 99%

Section: Uwb Ranging Selectionmentioning

confidence: 99%

Single UWB Anchor Aided PDR Heading and Step Length Correcting Indoor Localization System

et al. 2021

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“…Tian et al [ 18 ] utilized a Particle Filter (PF) to fuse PDR and UWB ranging; moreover, the anchor position was estimated in the initial stage to show that sensor drift was not significant. A ranging error model was then modified to implement PDR and UWB fusion using PF [ 30 ] to mitigate interference of NLoS. Cao et al [ 31 ] designed a UWB ranging and IMU fusion algorithm which used UWB ranging and heading (provided by IMU) to calculate target speed, and an extended Kalman filter to fuse IMU and UWB ranging constricted by estimated speed.…”

Section: Related Workmentioning

confidence: 99%

A CSI-Based Indoor Positioning System Using Single UWB Ranging Correction

Long

Kong

Zhang

et al. 2021

Sensors

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A fingerprint-based localization system is an economic way to solve an indoor positioning problem. However, the traditional off-line fingerprint collection stage is a time-consuming and laborious process which limits the use of fingerprint-based localization systems. In this paper, based on ubiquitous Wireless Fidelity (Wi-Fi) equipment and a low-cost Ultra-Wideband (UWB) ranging system (with only one UWB anchor), a ready-to-use indoor localization system is proposed to realize long-term and high-accuracy indoor positioning. More specifically, in this system, it is divided into two stages: (1) an initial stage, and (2) a positioning stage. In the initial stage, an Inertial Measure Unit (IMU) is used to calculate the position using Pedestrian Dead Reckon (PDR) algorithm within a preset number of steps, and the location-related fingerprints are collected to train a Convolutional Neural Network (CNN) regression model; simultaneously, in order to make the UWB ranging system adapt to the Non-Line-of-Sight (NLoS) environment, the increments of acceleration and angular velocity in IMU and the increments of single UWB ranging measures are correlated to pre-train a Supported Vector Regression (SVR). After reaching the threshold of time or step number, the system is changed into a positioning stage, and the CNN predicts the position calibrated by corrected UWB ranging. At last, a series of practical experiments are conducted in the real environment; the experiment results show that, due to the corrected UWB ranging measures calibrating the CNN parameters in every positioning period, this system has stable localization results in a comparative long-term range. Additionally, it has the advantages of stability, low cost, anti-noise, etc.

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“…Consequently, health care for elderly people has gradually received due attention and become a new research area (Li et al, 2016;Xu et al, 2018Xu et al, , 2019Zhuang et al, 2019c). As an important technology to assist indoor medical care, localization and tracking of target personnel appear in many works (Chen et al, 2020;Tian et al, 2020), and many approaches have been developed to provide the localization with sufficient accuracy.…”

Section: Introductionmentioning

confidence: 99%

Distributed Kalman filter for UWB/INS integrated pedestrian localization under colored measurement noise

Cao

Shmaliy

et al. 2021

Satell Navig

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Colored Measurement Noise (CMN) has a great impact on the accuracy of human localization in indoor environments with Inertial Navigation System (INS) integrated with Ultra Wide Band (UWB). To mitigate its influence, a distributed Kalman Filter (dKF) is developed for Gauss–Markov CMN with switching Colouredness Factor Matrix (CFM). In the proposed scheme, a data fusion filter employs the difference between the INS- and UWB-based distance measurements. The main filter produces a final optimal estimate of the human position by fusing the estimates from local filters. The effect of CMN is overcome by using measurement differencing of noisy observations. The tests show that the proposed dKF developed for CMN with CFM can reduce the localization error compared to the original dKF, and thus effectively improve the localization accuracy.

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An INS and UWB Fusion Approach With Adaptive Ranging Error Mitigation for Pedestrian Tracking

Cited by 36 publications

References 28 publications

Single UWB Anchor Aided PDR Heading and Step Length Correcting Indoor Localization System

Single UWB Anchor Aided PDR Heading and Step Length Correcting Indoor Localization System

A CSI-Based Indoor Positioning System Using Single UWB Ranging Correction

Distributed Kalman filter for UWB/INS integrated pedestrian localization under colored measurement noise

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