WLAN location determination via clustering and probability distributions

Youssef, Moustafa; Agrawala, Ashok K.; Shankar, A. Udaya

doi:10.1109/percom.2003.1192736

Cited by 702 publications

(527 citation statements)

References 12 publications

Supporting

Mentioning

526

Contrasting

Order By: Relevance

“…This was the first fingerprinting system that showed that it is possible to localize a laptop in the hallways of a small office building within 2-3 m of its true location, using fingerprints from four 802.11 access points. There have been improvements to Radar's fingerprint matching algorithm that have improved accuracy [2,13,19] and were able to differentiate between floors of a building with a high degree of precision [8]. In addition, commercial localization products have been built using 802.11 fingerprinting [20].…”

Section: Indoor Localization Using 80211 Fingerprintingmentioning

confidence: 99%

GSM indoor localization

Varshavsky

Lara

Hightower

et al. 2007

Pervasive and Mobile Computing

204

105

View full text Add to dashboard Cite

Accurate indoor localization has long been an objective of the ubiquitous computing research community, and numerous indoor localization solutions based on 802.11, Bluetooth, ultrasound and infrared technologies have been proposed. This paper presents the first accurate GSM indoor localization system that achieves median within floor accuracy of 4 m in large buildings and is able to identify the floor correctly in up to 60% of the cases and is within 2 floors in up to 98% of the cases in tall multi-floor buildings. We report evaluation results of two case studies conducted over a course of several years, with data collected from 6 buildings in 3 cities across North America. The key idea that makes accurate GSM-based indoor localization possible is the use of wide signalstrength fingerprints. In addition to the 6-strongest cells traditionally used in the GSM standard, the wide fingerprint includes readings from additional cells that are strong enough to be detected, but are too weak to be used for efficient communication. We further show that selecting a subset of highly relevant channels for fingerprinting matching out of all available channels, further improves the localization accuracy.

show abstract

Section: Indoor Localization Using 80211 Fingerprintingmentioning

confidence: 99%

GSM indoor localization

Varshavsky

Lara

Hightower

et al. 2007

Pervasive and Mobile Computing

204

105

View full text Add to dashboard Cite

show abstract

“…The second category uses empirical data to approximate the location (e.g., [1], [2], [17], [19], [20]). An early example of a location positioning system that uses empirical data is RADAR [2].…”

Section: Related Workmentioning

confidence: 99%

“…The Horus system [19] is similar to RADAR in that it uses sampled signal strengths from various locations. However, instead of using a mean of signal strength values, a signal strength probability function is created i.e., a function that calculates the probability of the signal strength of a access point at location (x,y).…”

Section: Related Workmentioning

confidence: 99%

“…The Horus system [19] is suppose to be able to distinguish locations about 1.4 meters apart with 90%. On the other hand, [1] reports that in a performance comparison Horus has 40% accuracy while Radar has 15% accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Examples of positioning systems are found in [1], [8], [9], [19], [17], [20]. Several of these systems provide high granularity (i.e., locations that are close to each other can be distinguished) but often incur a high cost of deployment since a thorough mapping of the environment is required in advance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

WLocator: An Indoor Positioning System

Phillips

Katchabaw

Lutfiyya

2007

Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2007)

View full text Add to dashboard Cite

Abstract-There exists a multitude of location-sensing systems utilizing wireless technology, though varying in cost, coverage and accuracy. In this paper we will introduce WLocator, a system that aims to strike a balance between these areas. WLocator will provide location information relative to symbolic, user-created "landmarks". These landmarks are easily created and maintained, thus reducing short and long term costs. Additionally, the unique matching algorithm invoked by WLocator attempts to offer a relatively high level of accuracy, given the low cost of implementing the system.

show abstract

A novel adaptive radio map for RSS‐based indoor positioning

Yang

et al. 2018

Concurrency and Computation

View full text Add to dashboard Cite

Summary Fingerprinting positioning is used for indoor location estimation because of low cost and open access properties, in which a radio map is built by calibrating signal‐strength values at several training locations in the offline phase. However, due to the sophisticated propagation of radio signals, the received signal strength (RSS) in wireless network change as the environment changes, and the radio map built in the offline phase may be out of date. Furthermore, the recalibration of signal‐strength values for each environment change is laborious and time consuming. In this paper, we present a novel algorithm to reconstruct a radio map using real‐time signal‐strength readings received at some reference points. We first demonstrate that different features of signal propagation are obtained in different regions of indoor environment. Then, the indoor environment is divided into several regions by clustering the path‐loss parameters of each reference point. In addition, the relationship between the RSS of reference points and calibration nodes is established with robust linear regression. Finally, the real‐time radio map is updated dynamically according to robust regression and the real‐time RSSs of the calibration nodes. The experimental results show the usefulness of the proposed method and the accuracy of the localization can be improved.

show abstract

WLAN location determination via clustering and probability distributions

Cited by 702 publications

References 12 publications

GSM indoor localization

GSM indoor localization

WLocator: An Indoor Positioning System

A novel adaptive radio map for RSS‐based indoor positioning

Contact Info

Product

Resources

About