An Infrastructure-Free Indoor Localization Algorithm for Smartphones

Accurate indoor positioning technology provides location-based service for a variety of applications. However, most existing indoor localization approaches (e.g., Wi-Fi and Bluetooth-based methods) rely heavily on positioning infrastructure, which prevents their large-scale deployment and limits the range at which they are applicable. Here, we proposed an infrastructure-free indoor positioning and tracking approach, termed LiMag, which used ubiquitous magnetic field and ambient lights (e.g., fluorescent, incandescent, and light-emitting diodes (LEDs)) without containing modulated information. We conducted an in-depth study on both the advantages and the challenges in leveraging magnetic field and ambient light intensity for indoor localization. Based on the insights from this study, we established a hybrid observation model that took full advantage of both the magnetic field and ambient light signals. To address the low discernibility of the hybrid observation model, LiMag first generated a single-step fingerprint model by vectorizing consecutive hybrid observations within each step. In order to accurately track users, a lightweight single-step tracking algorithm based on the single-step fingerprints and the particle filter framework was designed. LiMag leveraged the walking information of users and several single-step fingerprints to generate long trajectory fingerprints that exhibited much higher location differentiation ability than the single-step fingerprint. To accelerate particle convergence and eliminate the accumulative error of single-step tracking algorithm, a long trajectory calibration scheme based on long trajectory fingerprints was also introduced. An undirected weighted graph model was constructed to decrease the computational overhead resulting from this long trajectory matching. In addition to typical indoor scenarios including offices, shopping malls and parking lots, we also conducted experiments in more challenging scenarios, including large open-plan areas as well as environments characterized by strong sunlight. Our proposed algorithm achieved a 75th percentile localization accuracy of 1.8 m and 2.2 m, respectively, in the office and shopping mall tested. In conclusion, our LiMag algorithm provided location-based service of infrastructure-free with significantly improved localization accuracy and coverage, as well as satisfactory robustness inside complex indoor environments.

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THE DESIGN AND IMPLEMENTATION OF INDOOR LOCALIZATION SYSTEM USING MAGNETIC FIELD BASED ON SMARTPHONE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w7-379-2017 ◽

2017 ◽

Vol XLII-2/W7 ◽

pp. 379-383

Author(s):

J. Liu ◽

C. Jiang ◽

Z. Shi

Keyword(s):

Magnetic Field ◽

Indoor Localization ◽

Indoor Positioning ◽

Positioning System ◽

Localization Algorithm ◽

External Device ◽

Localization System ◽

Filtering Algorithm ◽

Coordinate Conversion ◽

Indoor Positioning System

Sufficient signal nodes are mostly required to implement indoor localization in mainstream research. Magnetic field take advantage of high precision, stable and reliability, and the reception of magnetic field signals is reliable and uncomplicated, it could be realized by geomagnetic sensor on smartphone, without external device. After the study of indoor positioning technologies, choose the geomagnetic field data as fingerprints to design an indoor localization system based on smartphone. A localization algorithm that appropriate geomagnetic matching is designed, and present filtering algorithm and algorithm for coordinate conversion. With the implement of plot geomagnetic fingerprints, the indoor positioning of smartphone without depending on external devices can be achieved. Finally, an indoor positioning system which is based on Android platform is successfully designed, through the experiments, proved the capability and effectiveness of indoor localization algorithm.

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GROF: Indoor Localization Using a Multiple-Bandwidth General Regression Neural Network and Outlier Filter

Sensors ◽

10.3390/s18113723 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3723 ◽

Cited By ~ 1

Author(s):

Zhang Chen ◽

Jinlong Wang

Keyword(s):

Neural Network ◽

Environmental Changes ◽

Indoor Localization ◽

Software Radio ◽

Indoor Positioning ◽

General Regression Neural Network ◽

Generalized Regression Neural Network ◽

K Nearest Neighbor ◽

Localization Accuracy ◽

Practical Applications

In recent years, a variety of methods have been developed for indoor localization utilizing fingerprints of received signal strength (RSS) that are location dependent. Nevertheless, the RSS is sensitive to environmental variations, in that the resulting fluctuation severely degrades the localization accuracy. Furthermore, the fingerprints survey course is time-consuming and labor-intensive. Therefore, the lightweight fingerprint-based indoor positioning approach is preferred for practical applications. In this paper, a novel multiple-bandwidth generalized regression neural network (GRNN) with the outlier filter indoor positioning approach (GROF) is proposed. The GROF method is based on the GRNN, for which we adopt a new kind of multiple-bandwidth kernel architecture to achieve a more flexible regression performance than that of the traditional GRNN. In addition, an outlier filtering scheme adopting the k-nearest neighbor (KNN) method is embedded into the localization module so as to improve the localization robustness against environmental changes. We discuss the multiple-bandwidth spread value training process and the outlier filtering algorithm, and demonstrate the feasibility and performance of GROF through experiment data, using a Universal Software Radio Peripheral (USRP) platform. The experimental results indicate that the GROF method outperforms the positioning methods, based on the standard GRNN, KNN, or backpropagation neural network (BPNN), both in localization accuracy and robustness, without the extra training sample requirement.

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SP-Loc: A crowdsourcing fingerprint based shop-level indoor localization algorithm integrating shop popularity without the indoor map

International Journal of Distributed Sensor Networks ◽

10.1177/1550147718815637 ◽

2018 ◽

Vol 14 (11) ◽

pp. 155014771881563 ◽

Cited By ~ 2

Author(s):

Jie Wei ◽

Fang Zhao ◽

Haiyong Luo

Keyword(s):

Indoor Localization ◽

Signal Strength ◽

Received Signal Strength ◽

Location Based Services ◽

Shopping Mall ◽

Time Stamp ◽

Localization Algorithm ◽

Variation Pattern ◽

Localization Accuracy ◽

User Location

With the development of indoor localization technology, the location-based services such as product advertising recommendation in the shopping mall attract widespread attention, as precise user location significantly improves the efficiency of advertising push and brings broader profits. However, most of the Wi-Fi-based indoor localization approaches requiring professionals to deploy expensive beacon devices and intensively collect fingerprints in each location grid, which severely limits its extensive promotion. We introduce a zero-cost indoor localization algorithm utilizing crowdsourcing fingerprints to obtain the shop recognition where the user is located. Naturally utilizing the Wi-Fi, GPS, and time-stamp fingerprints collected from the smartphone when user paid as the crowdsourcing fingerprint, we avoid the requirement for indoor map and get rid of both devices cost and manual signal collecting process. Moreover, a shop-level hierarchical indoor localization framework is proposed, and high robustness features based on Wi-Fi sequences variation pattern in the same shop analysis are designed to avoid the received signal strength fluctuations. Besides, we also pay more attention to mine the popularity properties of shops and explore GPS features to improve localization accuracy in the Wi-Fi absence situation effectively. Massive experiments indicate that SP-Loc achieves more than 93% localization accuracy.

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Using Acoustic Signal and Image to Achieve Accurate Indoor Localization

Sensors ◽

10.3390/s18082566 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2566 ◽

Cited By ~ 3

Author(s):

Rui Xi ◽

Daibo Liu ◽

Mengshu Hou ◽

Yujun Li ◽

Jun Li

Keyword(s):

Pervasive Computing ◽

Indoor Localization ◽

Practical Application ◽

Location Based Service ◽

Localization Accuracy ◽

Indoor Location ◽

Location Service ◽

Localization Scheme ◽

True Location ◽

Acoustic Sources

Location information plays a key role in pervasive computing and application, especially indoor location-based service, even though a mass of systems have been proposed, an accurate and practical indoor localization system remains unsettled. To tackle this issue, in this paper, we present a new localization scheme, SITE, combining acoustic Signals and Images to achieve accurate and robust indoor locaTion sErvice. Relying on a pre-deployed platform of acoustic sources with different frequencies, using proactively generated Doppler effect signals, SITE could track relative directions between the phone and the sources. Given m (m≥5) relative directions, SITE can use the angle differences to compute a set of locations corresponding to different subsets of sources. Then, based on a key observation—while the simultaneously estimated locations using different sets of acoustic anchors are within a small circle, the results converge to a point near the true location—SITE proposes a decision scheme that confirms whether these locations satisfy the demand of localization accuracy and can be used to search the user’s location. If not, SITE utilizes VSFM(Visual Structure from Motion) technique to achieve a set of relative locations using some images captured by the phone’s camera. By exploiting the synergy between the set of relative locations and the set of initial locations computed by relative directions, an optimal transformation relationship is obtained and applied to refine the initial calculated results. The refined result will be regarded as the user’s location. In the evaluation, we implemented a prototype and deployed a real platform of acoustic sources in different scenarios. Experimental results show that SITE has excellent performance of localization accuracy, robustness and feasibility in practical application.

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A Device-Free Indoor Localization Method Using CSI with Wi-Fi Signals

Sensors ◽

10.3390/s19143233 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3233 ◽

Cited By ~ 5

Author(s):

Xiaochao Dang ◽

Xuhao Tang ◽

Zhanjun Hao ◽

Yang Liu

Keyword(s):

Indoor Localization ◽

Indoor Positioning ◽

Location Based Service ◽

Channel State ◽

Position Information ◽

Fine Grained ◽

Positioning Method ◽

Fingerprint Database ◽

Device Free ◽

Limited Accuracy

Amid the ever-accelerated development of wireless communication technology, we have become increasingly demanding for location-based service; thus, passive indoor positioning has gained widespread attention. Channel State Information (CSI), as it can provide more detailed and fine-grained information, has been followed by researchers. Existing indoor positioning methods, however, are vulnerable to the environment and thus fail to fully reflect all the position features, due to limited accuracy of the fingerprint. As a solution, a CSI-based passive indoor positioning method was proposed, Wavelet Domain Denoising (WDD) was adopted to deal with the collected CSI amplitude, and the CSI phase information was unwound and transformed linearly in the offline phase. The post-processed amplitude and phase were taken as fingerprint data to build a fingerprint database, correlating with reference point position information. Results of experimental data analyzed under two different environments show that the present method boasts lower positioning error and higher stability than similar methods and can offer decimeter-level positioning accuracy.

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A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies

Sensors ◽

10.3390/s20247230 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7230

Author(s):

Santosh Subedi ◽

Jae-Young Pyun

Keyword(s):

Indoor Localization ◽

Indoor Positioning ◽

Performance Comparison ◽

Location Based Services ◽

Global Navigation Satellite Systems ◽

Smart Devices ◽

Positioning System ◽

Localization Accuracy ◽

Satellite Systems ◽

Indoor Positioning System

In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location information in outdoor environments. However, the GNSS signals are too weak to penetrate buildings and unable to provide reliable indoor LBS. Hence, GNSS’s incompetence in the indoor environment invites extensive research and development of an indoor positioning system (IPS). Various technologies and techniques have been studied for IPS development. This paper provides an overview of the available smartphone-based indoor localization solutions that rely on radio frequency technologies. As fingerprinting localization is mostly accepted for IPS development owing to its good localization accuracy, we discuss fingerprinting localization in detail. In particular, our analysis is more focused on practical IPS that are realized using a smartphone and Wi-Fi/Bluetooth Low Energy (BLE) as a signal source. Furthermore, we elaborate on the challenges of practical IPS, the available solutions and comprehensive performance comparison, and present some future trends in IPS development.

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A Context-Aware Assisted WiFi Positioning Method

Wireless Communications and Mobile Computing ◽

10.1155/2021/6683456 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Qi Feng ◽

Bing Jia ◽

Xi Luo ◽

Sha Li

Keyword(s):

Indoor Positioning ◽

Identification Algorithm ◽

Context Aware ◽

Limited Information ◽

Location Based Service ◽

Positioning Accuracy ◽

Localization Accuracy ◽

Variable Weight ◽

Positioning Method ◽

Weight Dynamic

With the convenience brought by Location-based service (LBS), users’ requirements for indoor positioning accuracy are getting higher than ever. However, many traditional indoor WiFi positioning methods may result in limited positioning accuracy because of the limited information of Received Signal Strength (RSS) of WiFi signal. This paper proposed a context-aware assisted WiFi positioning method (CAA-PM), which uses context information (i.e., light and sound) to assist WiFi-RSS for indoor positioning and uses an improved variable weight dynamic KNN fingerprint identification algorithm (VWD-KNN). Finally, experiments are carried out by using the dataset collected in both a closed laboratory and an open long corridor, and it is shown that the proposed algorithm substantially improves the localization accuracy comparing with other three classical algorithms.

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Low-Memory Indoor Positioning System for Standalone Embedded Hardware

Electronics ◽

10.3390/electronics10091059 ◽

2021 ◽

Vol 10 (9) ◽

pp. 1059

Author(s):

Han Jun Bae ◽

Lynn Choi

Keyword(s):

Flash Memory ◽

Spatial Information ◽

Indoor Localization ◽

Indoor Positioning ◽

Smart Devices ◽

Localization Accuracy ◽

Memory Schemes ◽

Positioning Algorithm ◽

Indoor Positioning System ◽

Indoor Spaces

As the proportion and importance of the indoor spaces in daily life are gradually increasing, spatial information and personal location information become more important in indoor spaces. In order to apply indoor positioning technologies in any places and for any targets inexpensively and easily, the system should utilize simple sensors and devices. In addition, due to the scalability, it is necessary to perform indoor positioning algorithms on the device itself, not on the server. In this paper, we construct standalone embedded hardware for performing the indoor positioning algorithm. We use the geomagnetic field for indoor localization, which does not require the installation of infrastructure and has more stable signal strength than RF RSS. In addition, we propose low-memory schemes based on the characteristics of the geomagnetic sensor measurement and convergence of the target’s estimated positions in order to implement indoor positioning algorithm to the hardware. We evaluate the performance in two testbeds: Hana Square (about 94 m × 26 m) and SK Future Hall (about 60 m × 38 m) indoor testbeds. We can reduce flash memory usage to 16.3% and 6.58% for each testbed and SRAM usage to 8.78% and 23.53% for each testbed with comparable localization accuracy to the system based on smart devices without low-memory schemes.

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