Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization

Chen, Zhenghua; Zou, Han; Jiang, Hao; Zhu, Qingchang; Soh, Yeng Chai; Xie, Lihua

doi:10.3390/s150100715

Cited by 332 publications

(231 citation statements)

References 25 publications

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“…Using location information, a more specific and detailed location-based classifier can be applied. There are well-known ways to obtain location information indoors [18,19] and outdoors [20,21]. In this study, however, the location information is not collected from the system and derived from the type of activities.…”

Section: Classifiermentioning

confidence: 99%

Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch

Kwon

Choi

2018

Wireless Communications and Mobile Computing

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Human activity recognition using wearable devices has been actively investigated in a wide range of applications. Most of them, however, either focus on simple activities wherein whole body movement is involved or require a variety of sensors to identify daily activities. In this study, we propose a human activity recognition system that collects data from an off-the-shelf smartwatch and uses an artificial neural network for classification. The proposed system is further enhanced using location information. We consider 11 activities, including both simple and daily activities. Experimental results show that various activities can be classified with an accuracy of 95%.

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Section: Classifiermentioning

confidence: 99%

Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch

Kwon

Choi

2018

Wireless Communications and Mobile Computing

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“…There have been many studies on sensor based positioning systems in the indoor environment [19,20,25]. [19] proposed a novel data fusion framework by using an extended Kalman filter to integrate WiFi localization with pedestrian dead reckoning.…”

Section: Sensor Based Positioning Systemmentioning

confidence: 99%

RFID Based Indoor Positioning System Using Event Filtering

Bok¹,

Yoo²

2017

Journal of Electrical Engineering and Technology

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-Recently, location systems using RFID technology have been studied in indoor environments. However, the existing techniques require high computational cost to compute the location of a moving object because they compare the location proximity of all reference tags and objects. In this paper, we propose an RFID based location positioning scheme using event filtering, which reduces the computation cost of calculating the locations of moving objects while maintaining the accuracy of location estimation. In addition, we propose an incremental location update policy to reduce the location update cost for moving objects. We also compare the proposed scheme with one of the localization schemes, LANDMARC using a performance evaluation. As a result, the proposed scheme outperforms LANDMARC in terms of the computational cost of location estimation. The proposed scheme also reduces the cost of location update by using the RFID-based update policy.

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“…There are several step length estimation methods that have been proposed for different applications in the past [21][22][23]. A comparison of several popular methods has revealed that the one proposed by Weinberg et al is best suited for a waist-mounted IMU [16,26].…”

Section: Knowledge-based Sle Algorithm Based On Fuzzy Logicmentioning

confidence: 99%

A Knowledge-Based Step Length Estimation Method Based on Fuzzy Logic and Multi-Sensor Fusion Algorithms for a Pedestrian Dead Reckoning System

Lai

Chang

Tsai

et al. 2016

IJGI

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Abstract:The demand for pedestrian navigation has increased along with the rapid progress in mobile and wearable devices. This study develops an accurate and usable Step Length Estimation (SLE) method for a Pedestrian Dead Reckoning (PDR) system with features including a wide range of step lengths, a self-contained system, and real-time computing, based on the multi-sensor fusion and Fuzzy Logic (FL) algorithms. The wide-range SLE developed in this study was achieved by using a knowledge-based method to model the walking patterns of the user. The input variables of the FL are step strength and frequency, and the output is the estimated step length. Moreover, a waist-mounted sensor module has been developed using low-cost inertial sensors. Since low-cost sensors suffer from various errors, a calibration procedure has been utilized to improve accuracy. The proposed PDR scheme in this study demonstrates its ability to be implemented on waist-mounted devices in real time and is suitable for the indoor and outdoor environments considered in this study without the need for map information or any pre-installed infrastructure. The experiment results show that the maximum distance error was within 1.2% of 116.51 m in an indoor environment and was 1.78% of 385.2 m in an outdoor environment.

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Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization

Cited by 332 publications

References 25 publications

Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch

Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch

RFID Based Indoor Positioning System Using Event Filtering

A Knowledge-Based Step Length Estimation Method Based on Fuzzy Logic and Multi-Sensor Fusion Algorithms for a Pedestrian Dead Reckoning System

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