A novel access point selection strategy for indoor location with Wi-Fi

Miao, Haoxuan; Wang, Zhanbin; Wang, Jingcheng; Zhang, Langwen; Liu, Zhengfeng

doi:10.1109/ccdc.2014.6853119

Cited by 18 publications

(17 citation statements)

References 10 publications

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“…Many existing AP selection methods normally select APs in a sampling stage or location stage [15]. In this paper, the APs selection method [16], [17] is used. In this method, an index for selecting the APs that can be used for positioning effectively is calculated.…”

Section: Access Points Selection Methodsmentioning

confidence: 99%

Assistive Approach for Pedestrian Dead Reckoning Based on RSSI Using Access Point Selection Method

Tateno

Cho

et al. 2018

SICE Journal of Control, Measurement, and System Integration

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Since smartphones have been widely adopted by the general public and wireless fundamental infrastructure has been changed greatly, indoor positioning and location tracking methods have been applied to various kinds of service and applications. Pedestrian dead-reckoning (PDR) and a received signal strength indicator (RSSI)-based positioning method are widely used since they can be easily utilized by smartphone with build-in sensors such as accelerometer and gyroscope. However, estimation error would occur to PDR which caused by environmental interference and accumulation error in angular calculation. Moreover, RSSI measurements fluctuate according to external effects. In this paper, a novel indoor localization method based on access point (AP) selection is proposed. By assisting PDR with RSSI from stable APs, the heading angle can be updated periodically to reduce the error. Furthermore, in order to enhance the accuracy, the angle is calculated using quaternion. The experimental results show that the suitable APs selection can improve the accuracy effectively.

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Section: Access Points Selection Methodsmentioning

confidence: 99%

Assistive Approach for Pedestrian Dead Reckoning Based on RSSI Using Access Point Selection Method

Tateno

Cho

et al. 2018

SICE Journal of Control, Measurement, and System Integration

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show abstract

“…Then, the distances between the observed RSS measurement and each cluster center are calculated, and the observed RSS measurement is classified into the most suitable subset of fingerprint maps with the minimum distance to the cluster center. Finally, the sparse representation of the RSS measurement is obtained by the CS algorithm in [11,12,13,14], and the final location is estimated by the positions of the RSS samples with non-zero sparse coefficients. The CS-based localization method utilizes the spatial correlation of the RSS fingerprint maps and produces better localization performance compared with KNN.…”

Section: Related Workmentioning

confidence: 99%

“…The key problem of the above localization is how to effectively find the matched or approximated RSS strengths in the fingerprint maps and estimate the position of the measurement with high precision. To overcome this problem, many researchers have proposed various localization algorithms, such as the KNN method [7,8,9], the Sparse Representation (SR)-based method [10], the Compressed Sensing (CS)-based method [11,12,13,14,15], etc. Although these fingerprint-based localization methods obtain acceptable positioning performance, most of the current localization methods do not explore and utilize the spatial correlation properties among fingerprint maps, as well as the temporal continuity of the measurements when the user is moving in his/her path.…”

Section: Introductionmentioning

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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“…The authors in [1] studied a system of access point selection that applying in Windows Systems for indoor locations with Wi-Fi. The Android system was introduced in [2][3][4] that applies the locating method.…”

Section: Introductionmentioning

confidence: 99%

An improved Android based industrial monitoring and locating system

Wang

Zhang

Hang

2015

The 27th Chinese Control and Decision Conference (2015 CCDC)

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With the sharp growth of smart phone applications in all industrial aspects, the advanced wireless monitoring system will replace the traditional monitoring equipments. An Android based monitoring and locating system has been proposed in this study. In this system, the wireless monitoring methods reduce the communication cost between hardwares. The system provides the convenient real time services of more accurate positions to any mobile phone without any preparation. Especially, it enhances the accuracy while distinguishing two short distance equipments. This system provides all the monitoring functions that can help control the instruments in a factory. Finally, the experiment results show that factory and other industry environment are located well in the tests.

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A novel access point selection strategy for indoor location with Wi-Fi

Cited by 18 publications

References 10 publications

Assistive Approach for Pedestrian Dead Reckoning Based on RSSI Using Access Point Selection Method

Assistive Approach for Pedestrian Dead Reckoning Based on RSSI Using Access Point Selection Method

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

An improved Android based industrial monitoring and locating system

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