Propagation model for the WLAN service at the campus environments

Kim, Ki Hong; Kim, Jung Ha; Yoon, Young Joong; Seok, Jae Ho; Lim, Jae Woo

doi:10.1109/vetecs.2003.1207529

The 57th IEEE Semiannual Vehicular Technology Conference, 2003. VTC 2003-Spring.

DOI: 10.1109/vetecs.2003.1207529

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Propagation model for the WLAN service at the campus environments

Ki Hong Kim

Jung Ha Kim

Young Joong Yoon

et al.

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“…From the receiver, Received Signal Strength Indication (RSSI) readings can be acquired for positioning use. The methods for Wi-Fi based positioning methods can roughly be categorized into two types: model based [13][14][15] and fingerprinting based [16][17][18]. Model based methods assume a path loss model to estimate the distances from the Access Points (APs) to the user.…”

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confidence: 99%

An Improved Pedestrian Ttracking Method Based on Wi-Fi Fingerprinting and Pedestrian Dead Reckoning

Bao

Tang

Guo

et al. 2020

Sensors

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Wi-Fi based positioning has great potential for use in indoor environments because Wi-Fi signals are near-ubiquitous in many indoor environments. With a Reference Fingerprint Map (RFM), fingerprint matching can be adopted for positioning. Much assisting information can be adopted for increasing the accuracy of Wi-Fi based positioning. One of the most adopted pieces of assisting information is the Pedestrian Dead Reckoning (PDR) information derived from inertial measurements. This is widely adopted because the inertial measurements can be acquired through a Commercial Off The Shelf (COTS) smartphone. To integrate the information of Wi-Fi fingerprinting and PDR information, many methods have adopted filters, such as Kalman filters and particle filters. A new methodology for integration of Wi-Fi fingerprinting and PDR is proposed using graph optimization in this paper. For the Wi-Fi based fingerprinting part, our method adopts the state-of-art hierarchical structure and the Penalized Logarithmic Gaussian Distance (PLGD) metric. In the integration part, a simple extended Kalman filter (EKF) is first used for integration of Wi-Fi fingerprinting and PDR results. Then, the tracking results are adopted as initial values for the optimization block, where Wi-Fi fingerprinting and PDR results are adopted to form an concentrated cost function (CCF). The CCF can be minimized with the aim of finding the optimal poses of the user with better tracking results. With both real-scenario experiments and simulations, we show that the proposed method performs better than classical Kalman filter based and particle filter based methods with both less average and maximum positioning error. Additionally, the proposed method is more robust to outliers in both Wi-Fi based and PDR based results, which is commonly seen in practical situations.

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mentioning

confidence: 99%

An Improved Pedestrian Ttracking Method Based on Wi-Fi Fingerprinting and Pedestrian Dead Reckoning

Bao

Tang

Guo

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

Optimization-Based Wi-Fi Radio Map Construction for Indoor Positioning Using Only Smart Phones

Tan

Fan

Wang

et al. 2018

Sensors

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Fingerprinting-based Wi-Fi indoor positioning has great potential for positioning in GPS-denied areas. However, establishing a fingerprinting map (also called a radio map) prior to positioning (site survey) is normally a labor-intensive task. This paper proposes a method for easy site survey without need for any extra hardware. The user can conduct the site survey adopting only a smart phone. The collected inertial-based readings are processed using the pedestrian dead-reckoning algorithms to generate a raw trajectory. Then a factor graph optimization method is proposed to re-estimate the trajectory by adding constraints originated from collected Wi-Fi fingerprints and landmark positions. The proposed method is verified through an experiment in a mall. The mean positioning error is 1.10 m and the maximum error is 2.25 m. This level of positioning accuracy is considered sufficient for radio map generation purposes. A classical baseline algorithm, the k-Nearest Neighbor (kNN) algorithm, is adopted to test the positioning performance of the radio map (RM), which also validates the quality of the constructed RM from the proposed method.

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Propagation model for the WLAN service at the campus environments

Cited by 2 publications

References 4 publications

An Improved Pedestrian Ttracking Method Based on Wi-Fi Fingerprinting and Pedestrian Dead Reckoning

An Improved Pedestrian Ttracking Method Based on Wi-Fi Fingerprinting and Pedestrian Dead Reckoning

Optimization-Based Wi-Fi Radio Map Construction for Indoor Positioning Using Only Smart Phones

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