An Infrastructure-Free Indoor Localization Algorithm for Smartphones

Wang, Qu; Luo, Haiyong; Men, Aidong; Zhao, Fang; Huang, Yan

doi:10.3390/s18103317

Cited by 23 publications

(11 citation statements)

References 43 publications

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“…The MLP-based regression model is constructed by adopting the Universal Approximation Theorem [45], which possesses fast training speed. MLP defines a mapping function, as shown in Equation (8), and obtains the best function approximation by learning the value of the parameter θ.…”

Section: Regression Model Using Mlpmentioning

confidence: 99%

“…Indoor positioning technology, such as Wi-Fi [1,2], magnetic [3,4], pedestrian dead reckoning [5,6] and visible light [7,8] technologies, have become increasingly more important in people's daily life, and positioning services have gradually become an indispensable mobile application. Most Wi-Fi based positioning technologies do not require deploying additional hardware because they only utilize Wi-Fi hotspots and existing wireless LANs (Wireless Local Area Networks) to obtain position estimation.…”

Section: Introductionmentioning

confidence: 99%

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A Robust Wi-Fi Fingerprint Positioning Algorithm Using Stacked Denoising Autoencoder and Multi-Layer Perceptron

Wang

Luo

et al. 2019

Remote Sensing

Self Cite

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With the increasing demand for location-based services, Wi-Fi-based indoor positioning technology has attracted much attention in recent years because of its ubiquitous deployment and low cost. Considering that Wi-Fi signals fluctuate greatly with time, extracting robust features of Wi-Fi signals is the key point to maintaining good positioning accuracy. To handle the dynamic fluctuation with time and sparsity of Wi-Fi signals, we propose an SDAE (Stacked Denoising Autoencoder)-based feature extraction method, which can obtain a robust and time-independent Wi-Fi fingerprint by learning the reconstruction distribution from a raw Wi-Fi signal and an artificial-noise-added Wi-Fi signal. We also leverage the strong representation ability of MLP (Multi-Layer Perceptron) to build a regression model, which maps the extracted features to the corresponding location. To fully evaluate the performance of our proposed algorithm, three datasets are applied, which represent three different scenarios, namely, spacious area with time interval, no time interval, and complex area with large time interval. The experimental results confirm the validity of our proposed SDAE-based feature extraction method, which can accurately reflect Wi-Fi signals in corresponding locations. Compared with other regression models, our proposed regression model can better map the extracted features to the target position. The average positioning error of our proposed algorithm is 4.24 m when there is a 52-day interval between training dataset and testing dataset. That confirms that the proposed algorithm outperforms other state-of-the-art positioning algorithms when there is a large time interval between training dataset and testing dataset. present, due to the wide deployment and availability of Wi-Fi infrastructure, Wi-Fi fingerprint-based localization has become one of the most dominant indoor positioning techniques.There are two main types of Wi-Fi-based indoor positioning technologies: RSSI (Received Signal Strength Indicator)-based ranging positioning algorithm [9][10][11], and fingerprint-based positioning algorithm [12][13][14]. The RSSI-based ranging positioning algorithm [11] usually adopts the received Wi-Fi signal to estimate the distance between the target (its location is unknown) and the access point (its location is known) using the wireless radio signal propagation model, and then estimates the target position using trilateration or multilateration methods. The fingerprint-based positioning algorithm [14] adopts the signal matching algorithm to estimate the user location. It first collects environmental Wi-Fi signals and constructs a Wi-Fi fingerprint database during the offline phase. During the online positioning phase, the fingerprint-based positioning algorithm compares the current Wi-Fi observation with the recorded fingerprint in the database to obtain the target position using the optimum matching criterion. Compared with the fingerprint-based positioning algorithm, the RSSI-based ranging positioning algorithm struggles to meet...

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Section: Regression Model Using Mlpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Robust Wi-Fi Fingerprint Positioning Algorithm Using Stacked Denoising Autoencoder and Multi-Layer Perceptron

Wang

Luo

et al. 2019

Remote Sensing

Self Cite

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“…In this paper, we understand indoor localisation to be an epitome of technologies and implementations for localisation in an enclosed environment. Examples of few such environments range from, but are not limited to, residential abodes [13], commercial shopping malls [160], industrial halls and factories [73], hospitals [64] and natural formations, such as underwater caves [99]. Here we consider sensor combinations stemming from the necessities imposed by these environments.…”

Section: Semantic Understanding Of Indoor Positioning and Trackingmentioning

confidence: 99%

Sensor Modalities and Fusion for Robust Indoor Localisation

Kozłowski¹,

Santos-Rodríguez²,

Piechocki³

2019

ICST Transactions on Ambient Systems

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The importance of accurate and efficient positioning and tracking is widely understood. However, there is a pressing lack of progress in the standardisation of methods, as well as generalised framework of their evaluation. The aim of this survey is to discuss the currently prevalent and emerging types of sensors used for location estimation. The intent of this review is to take account of this taxonomy and to provide a wider understanding of the current state-of-the-art. To that end, we outline various sensor modalities, as well as popular fusion and integration techniques, discussing how their combinations can help in various application settings. Firstly, we present the fundamental mechanics behind sensors employed by the localisation community. Furthermore we outline the formal theory behind prominent fusion methods and provide exhaustive implementation examples of each. Finally, we provide points for future discussion regarding localisation sensing, fusion and integration methods.to user's preference and experience. This makes the relative selection space large, and frequently open to interpretation with regard to available resources and constraints.Whilst the survey literature pertaining to localisation systems and methods is large [54,171,181], there exists very little in the way of localisation-centric sensor utilisation. This encompasses the use of bespoke [39] or off-the-shelf [13,118] sensors, specifically for the use of location estimation, robustification and optimisation. This area is extensive [20,35,57,83,136,146], yet very often bundled along with localisation technology surveys, without subsequent scrutiny. We aim to close this gap, by reviewing sensors, their fusion and utilisation as applied to localisation, in contrast to localisation methods, technologies and implementations themselves.Most of the existing localisation surveys include technology-specific reviews [30,54,92,181]. They concentrate upon the methods and algorithms related to indoor localisation [30,92], techniques and technologies [181]. Some work also addresses localisation from the perspective of the device itself, such as smartphones [171]. Xiao et al. study [171] is the most closely related work to our proposed examination. The main difference is, that instead of reviewing the devices as

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“…Therefore, in addition to providing more accurate motion level estimation, precise stride length estimation based on built-in smartphone inertial sensors enhances positioning accuracy of PDR. Most visible light positioning [5,6], Wi-Fi positioning [7][8][9], and magnetic positioning [10][11][12] critically depend on PDR. Hence, motion level estimation based on smartphones contributes to assisting and supporting patients undergoing health rehabilitation and treatment, activity monitoring of daily living, navigation, and numerous other applications [13].…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition

Wang

Luo

et al. 2019

Remote Sensing

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Stride length and walking distance estimation are becoming a key aspect of many applications. One of the methods of enhancing the accuracy of pedestrian dead reckoning is to accurately estimate the stride length of pedestrians. Existing stride length estimation (SLE) algorithms present good performance in the cases of walking at normal speed and the fixed smartphone mode (handheld). The mode represents a specific state of the carried smartphone. The error of existing SLE algorithms increases in complex scenes with many mode changes. Considering that stride length estimation is very sensitive to smartphone modes, this paper focused on combining smartphone mode recognition and stride length estimation to provide an accurate walking distance estimation. We combined multiple classification models to recognize five smartphone modes (calling, handheld, pocket, armband, swing). In addition to using a combination of time-domain and frequency-domain features of smartphone built-in accelerometers and gyroscopes during the stride interval, we constructed higher-order features based on the acknowledged studies (Kim, Scarlett, and Weinberg) to model stride length using the regression model of machine learning. In the offline phase, we trained the corresponding stride length estimation model for each mode. In the online prediction stage, we called the corresponding stride length estimation model according to the smartphone mode of a pedestrian. To train and evaluate the performance of our SLE, a dataset with smartphone mode, actual stride length, and total walking distance were collected. We conducted extensive and elaborate experiments to verify the performance of the proposed algorithm and compare it with the state-of-the-art SLE algorithms. Experimental results demonstrated that the proposed walking distance estimation method achieved significant accuracy improvement over existing individual approaches when a pedestrian was walking in both indoor and outdoor complex environments with multiple mode changes.

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An Infrastructure-Free Indoor Localization Algorithm for Smartphones

Cited by 23 publications

References 43 publications

A Robust Wi-Fi Fingerprint Positioning Algorithm Using Stacked Denoising Autoencoder and Multi-Layer Perceptron

A Robust Wi-Fi Fingerprint Positioning Algorithm Using Stacked Denoising Autoencoder and Multi-Layer Perceptron

Sensor Modalities and Fusion for Robust Indoor Localisation

Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition

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