Continuous Space Estimation: Increasing WiFi-Based Indoor Localization Resolution without Increasing the Site-Survey Effort

Hernández, Noelia; Ocaña, Manuel; Alonso, José M.; Kim, Euntai

doi:10.3390/s17010147

Cited by 67 publications

(40 citation statements)

References 48 publications

(60 reference statements)

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“…5. CSE: It is based on the method proposed in Hernández et al [46], in which an SVR is applied over the training data for a floor. From the regression results, per each AP and position, the RSS difference with the operational fingerprint is computed.…”

Section: Positioning With Simple Algorithmsmentioning

confidence: 99%

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Mendoza-Silva

Richter

Torres-Sospedra

et al. 2018

Data

126

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Abstract:WiFi fingerprinting, one of the most popular methods employed in indoor positioning, currently faces two major problems: lack of robustness to short and long time signal changes and difficult reproducibility of new methods presented in the relevant literature. This paper presents a WiFi RSS (Received Signal Strength) database created to foster and ease research works that address the above-mentioned two problems. A trained professional took several consecutive fingerprints while standing at specific positions and facing specific directions. The consecutive fingerprints may enable the study of short-term signals variations. The data collection spanned over 15 months, and, for each month, one type of training datasets and five types of test datasets were collected. The measurements of a dataset type (training or test) were taken at the same positions and directions every month, in order to enable the analysis of long-term signal variations. The database is provided with supporting materials and software, which give more information about the collection environment and eases the database utilization, respectively. The WiFi measurements and the supporting materials are available at the Zenodo repository under the open-source MIT license.

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Section: Positioning With Simple Algorithmsmentioning

confidence: 99%

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Mendoza-Silva

Richter

Torres-Sospedra

et al. 2018

Data

126

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“…As shown in Figure 15 , the method in the current work has better static positioning performance than methods in [ 20 , 21 ]. Table 5 further proves the validity of the proposed method.…”

Section: Experimentationmentioning

confidence: 80%

“…To verify the high accuracy performance of the proposed method under static positioning, experiments have been designed to compare the positioning results of the proposed method and the latest localization methods. In [ 20 ], the author proposed an approach based on support vector regression to estimate the received signal strength at non-site-surveyed positions of the environment. The proposed method could be used to improve the resolution of fingerprint-based indoor WiFi localization systems without increasing the site survey effort.…”

Section: Experimentationmentioning

confidence: 99%

A UWB/Improved PDR Integration Algorithm Applied to Dynamic Indoor Positioning for Pedestrians

Chen

2017

Sensors

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Inertial sensors are widely used in various applications, such as human motion monitoring and pedestrian positioning. However, inertial sensors cannot accurately define the process of human movement, a limitation that causes data drift in the process of human body positioning, thus seriously affecting positioning accuracy and stability. The traditional pedestrian dead-reckoning algorithm, which is based on a single inertial measurement unit, can suppress the data drift, but fails to accurately calculate the number of walking steps and heading value, thus it cannot meet the application requirements. This study proposes an indoor dynamic positioning method with an error self-correcting function based on the symmetrical characteristics of human motion to obtain the definition basis of human motion process quickly and to solve the abovementioned problems. On the basis of this proposed method, an ultra-wide band (UWB) method is introduced. An unscented Kalman filter is applied to fuse inertial sensors and UWB data, inertial positioning is applied to compensation for the defects of susceptibility to UWB signal obstacles, and UWB positioning is used to overcome the error accumulation of inertial positioning. The above method can improve both the positioning accuracy and the response of the positioning results. Finally, this study designs an indoor positioning test system to test the static and dynamic performances of the proposed indoor positioning method. Results show that the positioning system both has high accuracy and good real-time performance.

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“…The Wi-Fi fingerprinting method was used to correct the model. In order to improve the efficiency of Wi-Fi-based indoor localization, some new techniques have been recently adopted, such as the Light-Fidelity-assisted approach [18] and received signal strength estimation based on support vector regression [19]. …”

Section: Related Workmentioning

confidence: 99%

Activity Recognition and Semantic Description for Indoor Mobile Localization

Guo

Xiong

Zheng

et al. 2017

Sensors

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As a result of the rapid development of smartphone-based indoor localization technology, location-based services in indoor spaces have become a topic of interest. However, to date, the rich data resulting from indoor localization and navigation applications have not been fully exploited, which is significant for trajectory correction and advanced indoor map information extraction. In this paper, an integrated location acquisition method utilizing activity recognition and semantic information extraction is proposed for indoor mobile localization. The location acquisition method combines pedestrian dead reckoning (PDR), human activity recognition (HAR) and landmarks to acquire accurate indoor localization information. Considering the problem of initial position determination, a hidden Markov model (HMM) is utilized to infer the user’s initial position. To provide an improved service for further applications, the landmarks are further assigned semantic descriptions by detecting the user’s activities. The experiments conducted in this study confirm that a high degree of accuracy for a user’s indoor location can be obtained. Furthermore, the semantic information of a user’s trajectories can be extracted, which is extremely useful for further research into indoor location applications.

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Continuous Space Estimation: Increasing WiFi-Based Indoor Localization Resolution without Increasing the Site-Survey Effort

Cited by 67 publications

References 48 publications

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

A UWB/Improved PDR Integration Algorithm Applied to Dynamic Indoor Positioning for Pedestrians

Activity Recognition and Semantic Description for Indoor Mobile Localization

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