Wi-Fi/MARG Integration for Indoor Pedestrian Localization

Tian, Zengshan; Jin, Yue; Zhou, Mu; Wu, Zipeng; Li, Ze

doi:10.3390/s16122100

Cited by 55 publications

(12 citation statements)

References 33 publications

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“…This enzymatic fuel cell method has been previously validated [17] by comparing the data obtained with those found using a conventional catalase enzymatic biosensor, the assembly and functioning of which was described in detail in previous paper [24], and validated several times [23,24]. The validation of other three sensors was carried out and supplied by the producers of the commercial probes, using their suitably equipment devices [24,25], partly described in the previous paragraph 'apparatus'.…”

Section: Methodsmentioning

confidence: 99%

Study for developing an electronic tongue to discriminate three different classes of waters, by using common sensors and the principal component analysis

Tomassetti

Angeloni

Castrucci

et al. 2018

International Journal of Environmental Analytical Chemistry

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A basic research has been carried out using four probes to develop an electronic tongue able to discriminate three different kinds of natural waters, i.e. rain, river and groundwater. Several natural water samples were analysed to this purpose using a pH glass electrode, a digital (thermistor) thermometer, an amperometric gaseous diffusion oxygen sensor for measuring the dissolved oxygen and a direct methanol fuel cell enzymatic probe for measuring the concentration of alcoholic traces. Data were processed by means of principal component analysis, obtaining three well differentiated clusters, that demonstrate how the use of only four different sensors can discriminate in an inexpensive and effective way three different classes of natural waters.

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Section: Methodsmentioning

confidence: 99%

Study for developing an electronic tongue to discriminate three different classes of waters, by using common sensors and the principal component analysis

Tomassetti

Angeloni

Castrucci

et al. 2018

International Journal of Environmental Analytical Chemistry

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“…An indoor localization system was designed using smartphone built in magnetic, angular rate, gravity (MARG) sensors and a WiFi module. The study employed extended Kalman filter for the fusion of the data from two different signal sources [60].…”

Section: Proposed Algorithm Signal Source Error In Distancementioning

confidence: 99%

Indoor Positioning System: A New Approach Based on LSTM and Two Stage Activity Classification

et al. 2019

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The number of studies on the development of indoor positioning systems has increased recently due to the growing demands of the various location-based services. Inertial sensors available in commercial smartphones play an important role in indoor localization and navigation owing to their highly accurate localization performance. In this study, the inertial sensors of a smartphone, which generate distinct patterns for physical activities and action units (AUs), are employed to localize a target in an indoor environment. These AUs, (such as a left turn, right turn, normal step, short step, or long step), help to accurately estimate the indoor location of a target. By taking advantage of sophisticated deep learning algorithms, we propose a novel approach for indoor navigation based on long short-term memory (LSTM). The LSTM accurately recognizes physical activities and related AUs by automatically extracting the efficient features from the distinct patterns of the input data. Experiment results show that LSTM provides a significant improvement in the indoor positioning performance through the recognition task. The proposed system achieves a better localization performance than the trivial fingerprinting method, with an average error of 0.782 m in an indoor area of 128.6 m2. Additionally, the proposed system exhibited robust performance by excluding the abnormal activity from the pedestrian activities.

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“…The research used smartphone gyroscope sensor for heading estimation in the PDR system because of its advantages in the indoor environment of modern structures mostly composed of steel and concrete [8]. For obtaining the starting position in PDR, it is presented in several ways like Global Positioning System (GPS) tracking [9], identifying landmarks [10] and using RSSI in Wi-Fi fingerprint localization [11]- [12].…”

Section: Implementation Of Hybrid Indoor Positioning System Based On mentioning

confidence: 99%

Implementation of Hybrid Indoor Positioning System based on Wi-Fi and PDR in Smartphone

2019

IJRTE

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This paper proposed thehybridindoor positioning system in smartphone for positioning accuracy by fusion of wireless-fidelity (Wi-Fi) signals and inertial sensors from pedestrian dead reckoning (PDR) in smartphone. The proposed system uses Wi-Fi as the source of received signal strength indicator (RSSI) for fingerprint and smartphones sensor data from PDR. RSSI signals are used to determine the initial position and reduce error accumulation of PDR while smartphone sensor data are used to estimate user trajectory. Extended Kalman Filter (EKF) is the fusion algorithm used for its similarity with Kalman Filter (KF) but with advantages of processing non-linear progressions. An estimated 49 steps were detected which is identical to the 50 steps taken in the experiment while showing a trajectory similar to the actual route taken by the mobile user. A benefit of using built-in smartphone sensors is its cost-effectiveness and availability that does not require additional hardware. In addition, a nonlinear EKF is used to enhance the positioning accuracy in the proposed system. Further studies will be made in the potential of indoor positioning algorithm including the effect of noise interference on sensors and RSSI and the accumulated errors resulting from walking

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Wi-Fi/MARG Integration for Indoor Pedestrian Localization

Cited by 55 publications

References 33 publications

Study for developing an electronic tongue to discriminate three different classes of waters, by using common sensors and the principal component analysis

Study for developing an electronic tongue to discriminate three different classes of waters, by using common sensors and the principal component analysis

Indoor Positioning System: A New Approach Based on LSTM and Two Stage Activity Classification

Implementation of Hybrid Indoor Positioning System based on Wi-Fi and PDR in Smartphone

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