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AbstractÐFuture sub-THz cellular deployments may be utilized to complement the coverage of the global positioning system (GPS) and provide centimeter-level accuracy. In this work, we use measurement data at 142 GHz to test a map-based position location algorithm in an outdoor urban microcell (UMi) environment. We utilize an extended Kalman filter (EKF) to track the position of the user equipment (UE) along a rectangular track, with the transmitter-receiver separation distances varying from 24.3 m to 52.8 m. The position and velocity of the UE are tracked by the EKF, with measurements of the angle of arrival and time of flight information obtained along an outdoor track, to provide a mean accuracy of 24.8 cm at 142 GHz, over 34 UE locations, using a single base station in line-of-sight and non-line-of-sight.
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Smartphone based approximate localization using user highlighted texts from images
In many application scenarios, an approximate location can suffice instead of achieving high accuracy of GPS, or other network infrastructure enabled localization. This can lead to design of localization systems low in resource consumption, and faster in obtaining a result. In this work, we design and implement a lightweight localization system, called WhereAmI, that can perform coarse localization with low resource requirement. The key intuition behind this work is that a collection of nearby textual signs in an image representing a user’s surrounding forms a bag-of-words that provides a unique signature for her location. Due to the low performance of Optical Character Recognition (OCR) engine in outdoor settings, we develop a keyword-based positioning algorithm that can work even with partial errors in the detected texts representing business names. The partial errors in recognized business names are handled by using an n-gram-based text correction approach. We use a cloud based web service for offloading parts of the application workloads intelligently to save resources, like energy and network cost. The Android based prototype of WhereAmI is tested in uncontrolled environments. The experimental results show that WhereAmI can achieve 95% accuracy while consuming 20% less power than that of GPS. The proposed keyword-based positioning algorithm takes about 59 ms on average for returning the location. Keywords Outdoor positioningContent based image retrievalSignal based positioningSmartphone sensingDatabase searchPattern matchingEnergy efficiency
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- Award ID(s):
- 1650499
- PAR ID:
- 10054451
- Date Published:
- Journal Name:
- Pervasive and mobile computing
- Volume:
- 46
- Issue:
- June 2018
- ISSN:
- 1574-1192
- Page Range / eLocation ID:
- 1-17
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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