Proposals and Implementation of High Band IR-UWB for Increasing Propagation Distance for Indoor Positioning

Li, Huan-Bang; Nishikawa, Hisashi; Kagawa, Toshinori; Kojima, Fumihide

doi:10.1587/transfun.e101.a.185

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…e PDR positioning algorithm module mainly Complexity includes original data acquisition, data preprocessing, gait detection algorithm, step length estimation, heading angle estimation module, and position estimation [18]. e specific steps are as follows: (1) the acceleration and geomagnetic sensor data are acquired, followed by preprocessing.…”

Section: Low-power Ibeacon Network System Designmentioning

confidence: 99%

[Retracted] Low‐Power Indoor Positioning Algorithm Based on iBeacon Network

Zhang

Huai

2021

Complexity

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In this article, we use a low-power iBeacon network to conduct an in-depth analysis and research on the principle of indoor positioning and adopt an efficient and fast positioning algorithm. Secondly, based on the obtained analysis, an iBeacon-based indoor positioning system is proposed to analyze how to use iBeacon for accurate positioning and whether it can effectively compensate for the current mainstream positioning system. We analyze the requirements of the iBeacon-based indoor positioning system and propose the design of this positioning system. We analyze the platform and environment for software development, design the general framework of the positioning system, and analyze the logical structure of the whole system, the structure of data flow, and the communication protocols between each module of the positioning system. Then, we analyze the functions of the server module and the client module of the system, implement the functions of each module separately, and debug the functions of each module separately after each module is implemented. The feasibility of the algorithm and the performance improvement are confirmed by the experimental data. Our results show that the communication distance is improved by approximately 20.25% and the accuracy is improved by 5.62% compared to other existing results.

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Section: Low-power Ibeacon Network System Designmentioning

confidence: 99%

[Retracted] Low‐Power Indoor Positioning Algorithm Based on iBeacon Network

Zhang

Huai

2021

Complexity

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“…In addition, the received signal strength indication (RSSI) method [8], [9], [10], which obtains distance information from the amount of power attenuation for location estimation, and the fingerprint method [11], which utilizes the location dependency of channel state information (CSI) and RSSI from multiple base stations, have also been investigated. In particular, the TOA method that uses ultra-wideband (UWB) has attracted attention because of its superior ranging accuracy and low power consumption [12], [13]. Although these methods are highly accurate in line-of-sight (LOS) environments, which exhibit good visibility, it generally degrades in non-line-of-sight (NLOS) environments [14], [15], where visibility between sensor nodes and targets is blocked.…”

Section: Introductionmentioning

confidence: 99%

A Robust Indoor Localization Method for NLOS Environments Utilizing Sensor Subsets

Ishida

Okamoto

2022

IEEE Open J. Signal Process.

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Indoor location localization using the time of arrival (TOA) of ultra-wideband (UWB) radio waves faces a challenge in obtaining accurate TOA values in a non-line-of-sight (NLOS) environment owing to obstacles and multipath reception. The accuracy of localization using such sensor data in an NLOS environment is significantly degraded. To address this problem in NLOS environment, we proposed a new localization method that utilizes different sensor combinations for estimation and sensor placement topology. Based on the conventional localization method using various combinations of sensors, the estimation results containing an NLOS ranging value were first removed. Subsequently, we introduced a new parameter called the topological expectancy of localization accuracy (TELA), which represents the effect of sensor placement on the accuracy of localization. The final estimation result was then obtained using the weighted average of the remaining localization results obtained by TELA. Computer simulations and experimental results showed that the performance of the proposed method is superior by 4 to 37% compared with that of conventional methods, even in the presence of many NLOS environment sensors.INDEX TERMS Indoor localization, NLOS, TOA, cramer-rao lower bound, topological expectancy of localization accuracy (TELA).

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“…Since acceleration sensors cannot acquire static information, they have difficulties in the recognition of positions, shapes, and so on. In recent years, the indoor positioning systems have seen increasing development [18,19,20,21,22,23,24]. The Microsoft indoor positioning competition has attracted a large number of global teams from both companies and universities each year.…”

Section: Introductionmentioning

confidence: 99%

“…The Microsoft indoor positioning competition has attracted a large number of global teams from both companies and universities each year. The competition has witnessed lots of positioning technologies including wireless local area networks (WLAN) [19], Bluetooth low energy [20], optical light [21], radio frequency identification (RFID) [22], and UWB [23,24]. Among these methods, UWB is considered to be one of the most accurate approaches because it provides positioning estimation with centimeter-level accuracy [25,26].…”

Section: Introductionmentioning

confidence: 99%

A Posture Recognition Method Based on Indoor Positioning Technology

Huang

Wang

Zhang

et al. 2019

Sensors

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Posture recognition has been widely applied in fields such as physical training, environmental awareness, human-computer-interaction, surveillance system and elderly health care. The traditional methods consist of two main variations: machine vision methods and acceleration sensor methods. The former has the disadvantages of privacy invasion, high cost and complex implementation processes, while the latter has low recognition rate for still postures. A new body posture recognition scheme based on indoor positioning technology is presented in this paper. A single deployed indoor positioning system is constructed by installing wearable receiving tags at key points of the human body. The distance measurement method with ultra-wide band (UWB) radio is applied to position the key points of human body. Posture recognition is implemented by positioning. In the posture recognition algorithm, least square estimation (LSE) method and the improved extended Kalman filtering (iEKF) algorithm are respectively adopted to suppress the noise of the distances measurement and to improve the accuracy of positioning and recognition. The comparison of simulation results with the two methods shows that the improved extended Kalman filtering algorithm is more effective in error performance.

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Proposals and Implementation of High Band IR-UWB for Increasing Propagation Distance for Indoor Positioning

Cited by 9 publications

References 16 publications

[Retracted] Low‐Power Indoor Positioning Algorithm Based on iBeacon Network

[Retracted] Low‐Power Indoor Positioning Algorithm Based on iBeacon Network

A Robust Indoor Localization Method for NLOS Environments Utilizing Sensor Subsets

A Posture Recognition Method Based on Indoor Positioning Technology

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