Unsupervised Learning Technique to Obtain the Coordinates of Wi-Fi Access Points

Choi, Jeongsik; Choi, Yang-Seok; Talwar, Shilpa

doi:10.1109/ipin.2019.8911824

Cited by 22 publications

(15 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We collected the ranging results at each position considering all supported bandwidths, namely 20, 40, and 80 MHz, and set the burst mode with B = 8 to obtain more reliable distance measurements under the multipath propagation environment. 1 The APs are deployed in a sparse manner compared to our previous experiments, wherein 10 APs were placed in a 56 × 37 m 2 area [33], [34]. In this study, the density of an AP is decreased by half to cover a wider area using the same number of APs.…”

Section: Measurement Campaignsmentioning

confidence: 97%

Enhanced Wi-Fi RTT Ranging: A Sensor-Aided Learning Approach

Choi¹

2021

Preprint

Self Cite

View full text Add to dashboard Cite

The fine timing measurement (FTM) protocol is designed to determine precise ranging between Wi-Fi devices using round-trip time (RTT) measurements. However, the multipath propagation of radio waves generates inaccurate timing information, degrading the ranging performance. In this study, we use a neural network (NN) to adaptively learn the unique measurement patterns observed at different indoor environments and produce enhanced ranging outputs from raw FTM measurements. Moreover, the NN is trained based on an unsupervised learning framework, using the naturally accumulated sensor data acquired from users accessing location services. Therefore, the effort involved in collecting training data is significantly minimized. The experimental results verified that the collection of unlabeled data for a short duration is sufficient to learn the pattern in raw FTM measurements and produce improved ranging results. The proposed method reduced the ranging errors in raw distance measurements and well-calibrated ranging results requiring the collection of ground truth data by 47-50% and 17-29%, respectively. Consequently, positioning error reduced by 17-30% compared to the result with well-calibrated ranging.

show abstract

Section: Measurement Campaignsmentioning

confidence: 97%

Enhanced Wi-Fi RTT Ranging: A Sensor-Aided Learning Approach

Choi¹

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, errors on the receiver location while calibrating will subsequently lead to errors on the transmitter locations. If sufficient transmitter positions are known, the others can be extrapolated without extra measurements [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

Calibration of Visible Light Positioning Systems with a Mobile Robot

Amsters

Demeester

Stevens

et al. 2021

Sensors

View full text Add to dashboard Cite

Most indoor positioning systems require calibration before use. Fingerprinting requires the construction of a signal strength map, while ranging systems need the coordinates of the beacons. Calibration approaches exist for positioning systems that use Wi-Fi, radio frequency identification or ultrawideband. However, few examples are available for the calibration of visible light positioning systems. Most works focused on obtaining the channel model parameters or performed a calibration based on known receiver locations. In this paper, we describe an improved procedure that uses a mobile robot for data collection and is able to obtain a map of the environment with the beacon locations and their identities. Compared to previous work, the error is almost halved. Additionally, this approach does not require prior knowledge of the number of light sources or the receiver location. We demonstrate that the system performs well under a wide range of lighting conditions and investigate the influence of parameters such as the robot trajectory, camera resolution and field of view. Finally, we also close the loop between calibration and positioning and show that our approach has similar or better accuracy than manual calibration.

show abstract

“…One of the biggest challenges for this team to participate in the competition was that the provided training data do not include the coordinates of Wi-Fi access points, which are essential for range-based positioning solutions. Even though an automated way to acquire the coordinates of access points was studied in [36], this technique could not be applied for this competition as it needs to know the coordinates of a few access points as a reference. For this reason, the Wi-Fi fingerprinting technique was used instead of the range-based approach.…”

Section: ) Intel Labs Teammentioning

confidence: 99%