Database Correlation Method with Error Correction for Emergency Location

Juurakko, Suvi; Backman, W.

doi:10.1023/b:wire.0000049398.10614.02

Cited by 13 publications

(7 citation statements)

References 3 publications

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“…Fingerprinting techniques have also been developed for cellular positioning similar to those discussed for WiFi positioning (e.g. Juurakko and Backman 2004, Chen et al. 2006).…”

Section: Introductionmentioning

confidence: 99%

Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning

Zandbergen

2009

Transactions in GIS

356

206

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The 3G iPhone was the first consumer device to provide a seamless integration of three positioning technologies: Assisted GPS (A-GPS), WiFi positioning and cellular network positioning. This study presents an evaluation of the accuracy of locations obtained using these three positioning modes on the 3G iPhone. A-GPS locations were validated using surveyed benchmarks and compared to a traditional low-cost GPS receiver running simultaneously. WiFi and cellular positions for indoor locations were validated using high resolution orthophotography. Results indicate that A-GPS locations obtained using the 3G iPhone are much less accurate than those from regular autonomous GPS units (average median error of 8 m for ten 20-minute field tests) but appear sufficient for most Location Based Services (LBS). WiFi locations using the 3G iPhone are much less accurate (median error of 74 m for 58 observations) and fail to meet the published accuracy specifications. Positional errors in WiFi also reveal erratic spatial patterns resulting from the design of the calibration effort underlying the WiFi positioning system. Cellular positioning using the 3G iPhone is the least accurate positioning method (median error of 600 m for 64 observations), consistent with previous studies. Pros and cons of the three positioning technologies are presented in terms of coverage, accuracy and reliability, followed by a discussion of the implications for LBS using the 3G iPhone and similar mobile devices.

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“…Fingerprinting techniques have also been developed for cellular positioning similar to those discussed for WiFi positioning (e.g. Juurakko and Backman 2004, Chen et al. 2006).…”

Section: Introductionmentioning

confidence: 99%

Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning

Zandbergen

2009

Transactions in GIS

356

206

View full text Add to dashboard Cite

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“…Another localisation method category is fingerprinting [3], [6]- [9], [12] which creates the examined area radio map by performing RX signal strength measurements (drive tests). Measurements volume depends on the desired accuracy.…”

Section: Introductionmentioning

confidence: 99%

Mobile Terminal Subarea Localisation Method in GSM Networks

Mavrogeorgi

Xenou

Nikitopoulos

et al. 2007

2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications

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In this paper, a novel method for mobile terminal (MT) subarea localisation in GSM networks is presented. No base stations position and additional hardware are required. It is a variation of the fingerprinting localisation method aiming to determine not the MT geographic position but the MT subarea and to reduce necessary initialisation measurements. Several GSM-based methods have been developed for MT localisation.The localisation method, proposed in this paper, is based on the fingerprinting method but is differentiated regarding the produced result. In this method the location server does not compare the MT collected radio network measurements with the stored radio map but with the subareas proprietary characteristics indicating the subarea where the MT is located and not the exact MT geographic position. Finally, if an LBS requires as input the subscriber subarea and not the geographic position then the novel method presented here reduces significantly the localisation initialisation requirements and computational cost in order to provide the service.

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“…based on the "observed time difference of arrival" method (typical accuracy 100 m in densely covered areas) [12]. Methods with a theoretical accuracy of 25-50 m have also been suggested [13].…”

Section: Automated Data Collectionmentioning

confidence: 99%

Improving emergency response logistics through advanced GIS

Sterk

Praprotnik

2017

Open geospatial data, softw. stand.

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Background: Emergency call centres, commonly known as "112" or "911", are a crucial part of modern infrastructure. The location of the emergency, any related data, and good GIS support are required for their efficient operations. The location in particular is a vital piece of information but nevertheless often not obtained automatically at all or with insufficient accuracy, thus limiting both the speed and the reliability of emergency response. Methods: We present multiple improvements and novel features aimed at emergency response. A smart phone app speeds up and simplifies the acquisition of the location and other data. The typical problem of low penetration of such apps is solved by the web application accessible via an SMS. Common operational picture allows the operators to manage resources used throughout an intervention as well as review the past resource management. Statistical analysis strives to suggest the best course of action to the operators. Road access time calculations enable the analysis and improvement of emergency preparedness. Results: All our improvements, which are currently in various stages of development, were evaluated by the domain experts at the Slovenian Administration for Civil Protection and Disaster Relief. The automated data collection through web and native app, the calculation of road access times for emergency response units, and common operational picture management are already used in day-to-day operations. They have helped to find lost people, to establish new emergency response units where needed, and to manage resources in refugee camps. On the other hand, the suggested statistical approach to guiding call takers was discarded and suggested that the collected data rather be used for personnel training only. Conclusions: Most of our contributions to the emergency response process and software have proven valuable during actual emergencies, with certain shortcoming that still need to be addressed.

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Database Correlation Method with Error Correction for Emergency Location

Cited by 13 publications

References 3 publications

Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning

Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning

Mobile Terminal Subarea Localisation Method in GSM Networks

Improving emergency response logistics through advanced GIS

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