Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Providing Location Estimation within a Metropolitan Area Based on a Mobile Phone Network
DEXA '02 Proceedings of the 13th International Workshop on Database and Expert Systems Applications
Hybrid TDOA/AOA mobile user location for wideband CDMA cellular systems
IEEE Transactions on Wireless Communications
Mobile phone location determination and its impact on intelligent transportation systems
IEEE Transactions on Intelligent Transportation Systems
Position location using wireless communications on highways of the future
IEEE Communications Magazine
IEEE Communications Magazine
An Intra-domain Mobility Handling Scheme Across All-IP Wireless Networks
Wireless Personal Communications: An International Journal
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
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We propose a novel mobile positioning algorithm for cellular networks based on the estimation of the radio propagation environment. Since radio propagation characteristics vary in different environments, knowing the environment of the mobile user is essential for accurate Received Signal Strength-(RSS-) based location estimation. The key feature of our method is its capability to estimate the environment of the mobile user using machine learning techniques and to utilize this information for enhancing RSS-based distance calculations. The proposed algorithm, namely, EARBALE, has been evaluated using field measurements collected from a GSM network in diverse geographic locations. Our approach turns out to be significantly beneficial, enhancing estimation accuracy, and thereby enabling high-performance mobile positioning in a practical and cost-effective manner. Additionally, it is computationally light-weight and can be integrated onto any RSS-based algorithm as an enhancement add-on.