Method for yielding a database of location fingerprints in WLAN

Li, Binghao; Wang, Y.; Lee, H.K.; Dempster, Andrew G.; Rizos, Chris

doi:10.1049/ip-com:20050078

Cited by 214 publications

(143 citation statements)

References 12 publications

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“…The selection rule of the density of collection points of the fingerprint database data: the more intensive the selection of the fingerprint data, the higher positioning accuracy [11]. When using the cc2430 system of Texas Instrument and selecting a different interval data collection points in the test area, we find that if the intervals between the collection points are less than 0.5 m, RSSI values from the four references to nodes nearby several nearest data sampling points are the same.…”

Section: The Deployment Of Experimental Environmentmentioning

confidence: 96%

Advanced Indoor Positioning Using Zigbee Wireless Technology

Uradziński

Guo

Liu

et al. 2017

Wireless Pers Commun

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The paper presents the results of the project which examines the level of accuracy that can be achieved in precision indoor positioning using a new improved Zigbee network fingerprint method. This method can provide more accurate positioning by filtering algorithm for improving the fingerprint Zigbee database accuracy. In our experiment the following two steps have been completed. Firstly, we filtered out the interference data generated in the fingerprint database source data acquisition process what improved the accuracy of establishing the fingerprint database. Next, the nearest algorithm, the weighted nearest algorithm and Bayesian algorithm were used to calculate pedestrian's location, and then the results are compared and analyzed. As a result, the average error with the improved fingerprint database is less than or equal to 0.81 m in a long distance range. The accuracy of the results got much better compared with the results without filtering. A newly developed ZigBee system by us can be applied to the location based services in a bigger space inside the buildings (distances up to 40 m) or in the underground mines.

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Section: The Deployment Of Experimental Environmentmentioning

confidence: 96%

Advanced Indoor Positioning Using Zigbee Wireless Technology

Uradziński

Guo

Liu

et al. 2017

Wireless Pers Commun

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“…In this work we have chosen K to be 5 which has given good positioning result in WLAN positioning performed in [19]. Here the only processing required during the data collection phase is to group the GMDT samples according to the LTE serving BS ID.…”

Section: A K-nearest Neighbors Cluster-based Positioningmentioning

confidence: 99%

Cluster-based RF fingerprint positioning using LTE and WLAN outdoor signals

Mondal

Ristaniemi

Turkka

2015

2015 10th International Conference on Information, Communications and Signal Processing (ICICS)

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Abstract-In this paper we evaluate user-equipment (UE) positioning performance of three cluster-based RF fingerprinting methods using LTE and WLAN signals. Real-life LTE and WLAN data were collected for the evaluation purpose using consumer cellular-mobile handset utilizing 'Nemo Handy' drive test software tool. Test results of cluster-based methods were compared to the conventional grid-based RF fingerprinting. The cluster-based methods do not require grid-cell layout and training signature formation as compared to the gridbased method. They utilize LTE cell-ID searching technique to reduce the search space for clustering operation. Thus UE position estimation is done in short time with less computational cost. Among the cluster-based methods Agglomerative Hierarchical Cluster based RF fingerprinting provided best positioning accuracy using a single LTE and six WLAN signal strengths. This method showed an improvement of 42.3 % and 39.8 % in the 68th percentile and 95th percentile of positioning error (PE) over the grid-based RF fingerprinting.

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“…This method is used by most WLAN positioning systems, as it is able to compute accurate location estimates. It is the approach used by the positioning and tracking system proposed in this paper [16].…”

Section: Related Workmentioning

confidence: 99%

Localization using Neural Networks and Push-Pull Estimation based on RSS from AP to Mobile Device

Cho¹,

Lee²

2012

The KIPS Transactions:PartD

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Although the development of Global Positioning System (GPS) are more and more mature, its accuracy is just acceptable for outdoor positioning, not positioning for the indoor of building and the underpass. For the positioning application area for the indoor of building and the underpass, GPS even cannot achieve that accuracy because of the construction materials while the requirement for accurate positioning in the indoor of building and the underpass, because a space, a person is necessary, may be very small space with several square meters in the indoor of building and the underpass. The Received Signal Strength (RSS) based localization is becoming a good choice especially for the indoor of building and the underpass scenarios where the WiFi signals of IEEE 802.11, Wireless LAN, are available in almost every indoor of building and the underpass. The fundamental requirement of such localization system is to estimate location from Access Point (AP) to mobile device using RSS at a specific location. The Multi-path fading effects in this process make RSS to fluctuate unpredictably, causing uncertainty in localization. To deal with this problem, the combination for the method of Neural Networks and Push-Pull Estimation is applied so that the carried along the devices can learn and make the decision of position using mobile device where it is in the indoor of building and the underpass.

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Method for yielding a database of location fingerprints in WLAN

Cited by 214 publications

References 12 publications

Advanced Indoor Positioning Using Zigbee Wireless Technology

Advanced Indoor Positioning Using Zigbee Wireless Technology

Cluster-based RF fingerprint positioning using LTE and WLAN outdoor signals

Localization using Neural Networks and Push-Pull Estimation based on RSS from AP to Mobile Device

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