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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 2/2015

01.01.2015

Location Prediction of Vehicles in VANETs Using A Kalman Filter

verfasst von: Huifang Feng, Chunfeng Liu, Yantai Shu, Oliver W. W. Yang

Erschienen in: Wireless Personal Communications | Ausgabe 2/2015

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Abstract

Location information is very important for many applications of vehicular networks such as routing, network management, data dissemination protocols, road congestion, etc. If some reliable prediction is done on vehicle’s next move, then resources can be allocated optimally as the vehicle moves around. This would increase the performance of VANETs. A Kalman filter is employed for predicting the vehicle’s future location in this paper. We conducted experiments using both real vehicle mobility traces and model-driven traces. We quantitatively compare the prediction performance of a Kalman filter and neural network-based methods. In all traces, the proposed model exhibits superior prediction accuracy than the other prediction schemes.
Vorheriger Artikel The Performance of Systems Featuring Dynamic Decode-and-Forward and Network Coding
Nächster Artikel A Modified Optimally Quantized Offset Min-Sum Decoding Algorithm for Low-Complexity LDPC Decoder

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Metadaten
Titel
Location Prediction of Vehicles in VANETs Using A Kalman Filter
verfasst von
Huifang Feng
Chunfeng Liu
Yantai Shu
Oliver W. W. Yang
Publikationsdatum
01.01.2015
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 2/2015
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-014-2025-3

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