Atmospheric pressure, and temperature aloft

Burton, B. J.

doi:10.1002/j.1477-8696.1993.tb05867.x

Cited by 7 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have used the same smartphone to collect the Wi-Fi, magnetic and barometer data. The hypsometric equation [55] is used to perform barometric floor identification as follows, h=((PoP)15.257−1)×(T+273.15)0.0065. where P is the current atmospheric pressure, Po is the sea-level pressure which is 1013.25 hPa, h is the altitude, and T is the temperature. We have used T as 15 °C in Equation (12).…”

Section: Experiments and Resultsmentioning

confidence: 99%

Floor Identification Using Magnetic Field Data with Smartphone Sensors

Ashraf

Hur

Shafiq

et al. 2019

Sensors

View full text Add to dashboard Cite

Floor identification plays a key role in multi-story indoor positioning and localization systems. Current floor identification systems rely primarily on Wi-Fi signals and barometric pressure data. Barometric systems require installation of additional standalone sensors to perform floor identification. Wi-Fi systems, on the other hand, are vulnerable to the dynamic environment and adverse effects of path loss, shadowing, and multipath fading. In this paper, we take advantage of a pervasive magnetic field to compensate for the limitations of these systems. We employ smartphone sensors to make the proposed scheme infrastructure free and cost-effective. We use smartphone magnetic sensors to identify the floors in a multi-story building with improved accuracy. Floor identification is performed with user activities of normal walking, call listening, and phone swinging. Various machine learning techniques are leveraged to identify user activities. Extensive experiments are performed to evaluate the proposed magnetic-data-based floor identification scheme. Additionally, the impact of device heterogeneity on floor identification is investigated using Samsung Galaxy S8, LG G6, and LG G7 smartphones. Research results demonstrate that the magnetic floor identification outperforms barometric and Wi-Fi-enabled floor detection techniques. A floor change module is incorporated to further enhance the accuracy of floor identification.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Floor Identification Using Magnetic Field Data with Smartphone Sensors

Ashraf

Hur

Shafiq

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Barometer, though it cannot be deployed as an individual sensor for localization, often proves to be very influential to determine the change in altitude for PDR localization. It measures the atmospheric pressure and uses the hypsometeric equation to measure the altitude [172]:…”

Section: Lux Meter and Barometermentioning

confidence: 99%

Smartphone Sensor Based Indoor Positioning: Current Status, Opportunities, and Future Challenges

2020

View full text Add to dashboard Cite

The last two decades have witnessed a rich variety of indoor positioning and localization research. Starting with Microsoft Research pioneering the fingerprint approach based RADAR, MIT’s Cricket, and then moving towards beacon-based localization are few among many others. In parallel, researchers looked into other appealing and promising technologies like radio frequency identification, ultra-wideband, infrared, and visible light-based systems. However, the proliferation of smartphones over the past few years revolutionized and reshaped indoor localization towards new horizons. The deployment of MEMS sensors in modern smartphones have initiated new opportunities and challenges for the industry and academia alike. Additionally, the demands and potential of location-based services compelled the researchers to look into more robust, accurate, smartphone deployable, and context-aware location sensing. This study presents a comprehensive review of the approaches that make use of data from one or more sensors to estimate the user’s indoor location. By analyzing the approaches leveraged on smartphone sensors, it discusses the associated challenges of such approaches and points out the areas that need considerable research to overcome their limitations.

show abstract

“…It is usually 'mobile', and much smaller than the warm high. Another variety of cold anticyclone forms over continents in winter where intense radiational cooling of the lowest layers leads to accumulation of extremely cold dense air near the surface ~ the exact reverse of the heat-low process described by Burton (1993). The winter-time Siberian high is the classic example of this latter type.…”

Section: What Is An Anticyclone?mentioning

confidence: 99%