Pedestrian Dead-Reckoning Indoor Localization Based on OS-ELM

Zhang, Mingyang; Wen, Yingyou; Chen, Jian; Yang, Xiaodong; Gao, Rui; Zhao, Hong

doi:10.1109/access.2018.2791579

Cited by 58 publications

(33 citation statements)

References 33 publications

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“…We thus conclude that it is a 76-sparse signal in DWT domain. We further took a raw RSSI measurement of an access point (AP) from a smartphone detected in a real environment [ 36 ]. Due to the channel-path fading and interference from nearby equipment, it was hard to find an orthonormal basis from the DWT domain, as shown in Figure 6 a.…”

Section: Numerical Resultsmentioning

confidence: 99%

Adaptive Compressive Sensing and Data Recovery for Periodical Monitoring Wireless Sensor Networks

Chen

Jia

Deng

et al. 2018

Sensors

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The development of compressive sensing (CS) technology has inspired data gathering in wireless sensor networks to move from traditional raw data gathering towards compression based gathering using data correlations. While extensive efforts have been made to improve the data gathering efficiency, little has been done for data that is gathered and recovered data with unknown and dynamic sparsity. In this work, we present an adaptive compressive sensing data gathering scheme to capture the dynamic nature of signal sparsity. By only re-sampling a few measurements, the current sparsity as well as the new sampling rate can be accurately determined, thus guaranteeing recovery performance and saving energy. In order to recover a signal with unknown sparsity, we further propose an adaptive step size variation integrated with a sparsity adaptive matching pursuit algorithm to improve the recovery performance and convergence speed. Our simulation results show that the proposed algorithm can capture the variation in the sparsities of the original signal and obtain a much longer network lifetime than traditional raw data gathering algorithms.

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Section: Numerical Resultsmentioning

confidence: 99%

Adaptive Compressive Sensing and Data Recovery for Periodical Monitoring Wireless Sensor Networks

Chen

Jia

Deng

et al. 2018

Sensors

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“…The LKF used in [33] is used for heading sensor fusion. The position estimation algorithm proposed in [35] is used for current user position estimation.…”

Section: Pdr Positioningmentioning

confidence: 99%

A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements

2019

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Sensor fusion frameworks for indoor localization are developed with the specific goal of reducing positioning errors. Although many conventional localization frameworks without fusion have been improved to reduce positioning error, sensor fusion frameworks generally provide a further improvement in positioning accuracy. In this paper, we propose a sensor fusion framework for indoor localization using the smartphone inertial measurement unit (IMU) sensor data and Wi-Fi received signal strength indication (RSSI) measurements. The proposed sensor fusion framework uses location fingerprinting and trilateration for Wi-Fi positioning. Additionally, a pedestrian dead reckoning (PDR) algorithm is used for position estimation in indoor scenarios. The proposed framework achieves a maximum of 1.17 m localization error for the rectangular motion of a pedestrian and a maximum of 0.44 m localization error for linear motion.

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“…Besides the use of ELM in WiFi-based positioning, many experts, such as the authors of [23], have also employed it in positioning based on personal dead reckoning (PDR). Initially, these authors created the PDR positioning procedure as an estimation function.…”

Section: Related Workmentioning

confidence: 99%

MFA-OSELM Algorithm for WiFi-Based Indoor Positioning System

et al. 2019

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Indoor localization is a dynamic and exciting research area. WiFi has exhibited a tremendous capability for internal localization since it is extensively used and easily accessible. Facilitating the use of WiFi for this purpose requires fingerprint formation and the implementation of a learning algorithm with the aim of using the fingerprint to determine locations. The most difficult aspect of techniques based on fingerprints is the effect of dynamic environmental changes on fingerprint authentication. With the aim of dealing with this problem, many experts have adopted transfer-learning methods, even though in WiFi indoor localization the dynamic quality of the change in the fingerprint has some cyclic factors that necessitate the use of previous knowledge in various situations. Thus, this paper presents the maximum feature adaptive online sequential extreme learning machine (MFA-OSELM) technique, which uses previous knowledge to handle the cyclic dynamic factors that are brought about by the issue of mobility, which is present in internal environments. This research extends the earlier study of the feature adaptive online sequential extreme learning machine (FA-OSELM). The results of this research demonstrate that MFA-OSELM is superior to FA-OSELM given its capacity to preserve previous data when a person goes back to locations that he/she had visited earlier. Also, there is always a positive accuracy change when using MFA-OSELM, with the best change achieved being 27% (ranging from eight to 27% and six to 18% for the TampereU and UJIIndoorLoc datasets, respectively), which proves the efficiency of MFA-OSELM in restoring previous knowledge.

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Pedestrian Dead-Reckoning Indoor Localization Based on OS-ELM

Cited by 58 publications

References 33 publications

Adaptive Compressive Sensing and Data Recovery for Periodical Monitoring Wireless Sensor Networks

Adaptive Compressive Sensing and Data Recovery for Periodical Monitoring Wireless Sensor Networks

A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements

MFA-OSELM Algorithm for WiFi-Based Indoor Positioning System

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