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A Markov Model for Dynamic Behavior of Toa-Based Ranging in Indoor Localization
The existence of undetected direct path ( UDP) conditions causes occurrence of unexpected large random ranging errors which pose a serious challenge to precise indoor localization using time of arrival ( ToA). Therefore, analysis of the behavior of the ranging error is essential for the design of precise ToA-based indoor localization systems. In this paper, we propose a novel analytical framework for the analysis of the dynamic spatial variations of ranging error observed by a mobile user based on an application of Markov chain. the model relegates the behavior of ranging error into four main categories associated with four states of the Markov process. the parameters of distributions of ranging error in each Markov state are extracted from empirical data collected from a measurement calibrated ray tracing ( RT) algorithm simulating a typical office environment. the analytical derivation of parameters of the Markov model employs the existing path loss models for the first detected path and total multipath received power in the same office environment. Results of simulated errors from the Markov model and actual errors from empirical data show close agreement
Analysis of Ultra Wide Band (UWB) Technology for an Indoor Geolocation and Physiological Monitoring System
The goal of this research is to analyze the utility of UWB for indoor geolocation and to evaluate a prototype system, which will send information detailing a person’s position and physiological status to a command center. In a real world environment, geolocation and physiological status information needs to be sent to a command and control center that may be located several miles away from the operational environment. This research analyzes and characterizes the UWB signal in the various operational environments associated with indoor geolocation. Additionally, typical usage scenarios for the interaction between UWB and other devices are also tested and evaluated
Channel Sounding for the Masses: Low Complexity GNU 802.11b Channel Impulse Response Estimation
New techniques in cross-layer wireless networks are building demand for
ubiquitous channel sounding, that is, the capability to measure channel impulse
response (CIR) with any standard wireless network and node. Towards that goal,
we present a software-defined IEEE 802.11b receiver and CIR estimation system
with little additional computational complexity compared to 802.11b reception
alone. The system implementation, using the universal software radio peripheral
(USRP) and GNU Radio, is described and compared to previous work. By overcoming
computational limitations and performing direct-sequence spread-spectrum
(DS-SS) matched filtering on the USRP, we enable high-quality yet inexpensive
CIR estimation. We validate the channel sounder and present a drive test
campaign which measures hundreds of channels between WiFi access points and an
in-vehicle receiver in urban and suburban areas
Accurate Positioning in Ultra-Wideband Systems
Cataloged from PDF version of article.Accurate positioning systems can be realized via ultra-wideband signals due to their high time resolution. In this article, position estimation is studied for UWB systems. After a brief introduction to UWB signals and their positioning applications,
two-step positioning systems are investigated from a UWB perspective. It is observed that time-based positioning is well suited for UWB systems. Then time-based UWB ranging is studied in detail, and the main challenges, theoretical limits, and range estimation algorithms are presented. Performance of some practical time-based ranging algorithms is investigated and compared against the maximum likelihood estimator and the theoretical limits. The trade-off between complexity and accuracy is .observe
PlaNet - Photo Geolocation with Convolutional Neural Networks
Is it possible to build a system to determine the location where a photo was
taken using just its pixels? In general, the problem seems exceptionally
difficult: it is trivial to construct situations where no location can be
inferred. Yet images often contain informative cues such as landmarks, weather
patterns, vegetation, road markings, and architectural details, which in
combination may allow one to determine an approximate location and occasionally
an exact location. Websites such as GeoGuessr and View from your Window suggest
that humans are relatively good at integrating these cues to geolocate images,
especially en-masse. In computer vision, the photo geolocation problem is
usually approached using image retrieval methods. In contrast, we pose the
problem as one of classification by subdividing the surface of the earth into
thousands of multi-scale geographic cells, and train a deep network using
millions of geotagged images. While previous approaches only recognize
landmarks or perform approximate matching using global image descriptors, our
model is able to use and integrate multiple visible cues. We show that the
resulting model, called PlaNet, outperforms previous approaches and even
attains superhuman levels of accuracy in some cases. Moreover, we extend our
model to photo albums by combining it with a long short-term memory (LSTM)
architecture. By learning to exploit temporal coherence to geolocate uncertain
photos, we demonstrate that this model achieves a 50% performance improvement
over the single-image model
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