Wi-Fi Fingerprint Positioning Updated by Pedestrian Dead Reckoning for Mobile Phone Indoor Localization

Chang, Qiang; Velde, Samuel Van de; Wei-ping, Wang; Li, Qun; Hou, Hongtao; Steendam, Heidi

doi:10.1007/978-3-662-46632-2_63

Cited by 24 publications

(22 citation statements)

References 9 publications

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“…The authors employed WiFi fingerprinting integrated with PDR for the indoor navigation and localization. K-weighted nearest node algorithm was also used in combination with PDR for improving the positioning accuracy [59].…”

Section: Rss Of Wifi and Orientation 146 Mmentioning

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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Section: Rss Of Wifi and Orientation 146 Mmentioning

confidence: 99%

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

et al. 2019

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“…The SSFM-based localization method is an attractive topic in wireless indoor localization [25,26,27,28,29]. Chang et al [25] integrated Pedestrian Dead Reckoning (PDR) with WiFi fingerprinting to provide an accurate positioning algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Chang et al [25] integrated Pedestrian Dead Reckoning (PDR) with WiFi fingerprinting to provide an accurate positioning algorithm. Park et al [26] proposed a method to collect off-line data effectively in a fingerprinting-based indoor location estimation system based on using Kalman filtering.…”

Section: Related Workmentioning

confidence: 99%

RSS Fingerprint Based Indoor Localization Using Sparse Representation with Spatio-Temporal Constraint

Piao

Zhang

et al. 2016

Sensors

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The Received Signal Strength (RSS) fingerprint-based indoor localization is an important research topic in wireless network communications. Most current RSS fingerprint-based indoor localization methods do not explore and utilize the spatial or temporal correlation existing in fingerprint data and measurement data, which is helpful for improving localization accuracy. In this paper, we propose an RSS fingerprint-based indoor localization method by integrating the spatio-temporal constraints into the sparse representation model. The proposed model utilizes the inherent spatial correlation of fingerprint data in the fingerprint matching and uses the temporal continuity of the RSS measurement data in the localization phase. Experiments on the simulated data and the localization tests in the real scenes show that the proposed method improves the localization accuracy and stability effectively compared with state-of-the-art indoor localization methods.

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“…Machine learning is a method of using algorithms to parse data and then mine the underlying rules in the data to make decisions or predictions about certain things. In recent years, machine learning has been applied to the Wi-Fibased positioning method and has achieved an excellent positioning effect [8][9][10][11][12]. Liao et al [13] combined the decision tree classification model with indoor maps to reduce the dependence on hardware devices and improve the accuracy and reliability of the system.…”

Section: Introductionmentioning

confidence: 99%

A method for indoor Wi-Fi location based on improved back propagation neural network

Chen¹,

Dong²,

He³

et al. 2019

Turk J Elec Eng & Comp Sci

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In order to achieve high precision on indoor location, a Wi-Fi indoor location method based on improved back propagation (BP) neural network is proposed. The classical BP neural network is optimized in real time by the ant colony optimization algorithm. Meanwhile, the momentum term is introduced to construct an improved four-layer BP neural network model. The model uses the Wi-Fi signal feature as the input of the BP neural network and succeeds in the area classification under multiple Wi-Fi signal features. The experimental results demonstrate that the improved BP neural network can increase the classification accuracy of the classifier effectively, and achieve a high-precision indoor area location. Furthermore, it performs better practical results while ensuring the time complexity. The advantages of this method are high practicability, low cost, high prediction classification accuracy, and robust stability, which can achieve the efficient classification of the short-range area.

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Wi-Fi Fingerprint Positioning Updated by Pedestrian Dead Reckoning for Mobile Phone Indoor Localization

Cited by 24 publications

References 9 publications

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

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

RSS Fingerprint Based Indoor Localization Using Sparse Representation with Spatio-Temporal Constraint

A method for indoor Wi-Fi location based on improved back propagation neural network

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