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• W2972865660 abstract "Autonomous driving vehicles shall increase the efficiency of passenger and goods transportation. Connecting these vehicles and ensuring the reliable exchange of safety critical data is one of the biggest challenges nowadays. The basis of reliable communication between vehicles is a fundamental understanding of the propagation mechanism and the resulting channel models. For the communication between moving vehicles geometry-based stochastic channel models (GSCMs) are widely used to model the non-stationary channel processes. To understand the underlying geometry between transmitter, receiver and scatterers, we propose a joint delay and Doppler frequency estimation to localize scatterer. We use train-to-train (T2T) measurement data and estimate the delay and the Doppler frequency for each measurement of each received signal. The probability density function (PDF) of the joint delay and Doppler frequency estimation is transformed to the Cartesian domain and plotted on a geo-referenced satellite image. In this way, the estimated scatterer position and the related propagation characteristics can be assigned to real objects." @default.
• W2972865660 created "2019-09-19" @default.
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• W2972865660 date "2019-03-01" @default.
• W2972865660 modified "2023-09-26" @default.
• W2972865660 title "Joint Delay and Doppler Frequency Estimation for Scatterer Localization in Railway Environments" @default.
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• W2972865660 hasPublicationYear "2019" @default.
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