Algorithms and Architectures
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Automatic Generation of Neural Network Architecture Using Evolutionary Computation
Automatic Generation of Neural Network Architecture Using Evolutionary Computation
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Design of indoor positioning systems based on location fingerprinting technique
Design of indoor positioning systems based on location fingerprinting technique
Neural Networks
Prediction of outdoor and outdoor-to-indoor coverage in urban areas at 1.8 GHz
IEEE Journal on Selected Areas in Communications
Active GSM cell-id tracking: "Where Did You Disappear?"
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
Performance study of active tracking in a cellular network using a modular signaling platform
Proceedings of the 8th international conference on Mobile systems, applications, and services
Comparing LR, GP, BPN, RBF and SVR for Self-Learning Pattern Matching in WSN Indoor Localization
International Journal of Applied Metaheuristic Computing
Active tracking in mobile networks: An in-depth view
Computer Networks: The International Journal of Computer and Telecommunications Networking
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With growth of world wide cellular connections, localization within cellular network will play a central role in enabling value-added services. This is a huge revenue opportunity for mobile operators. Existing location techniques have poor performance in urban and indoor environments due to severe multipath and NLOS propagations, which are significant for those areas. Fingerprint-based methods have been preferred for those types of environments. In this work, a fingerprint-based technique is developed and applied. RSSI data used during training phase of the pattern-matching system are generated from the so-called in this work Thomas Kuerner propagation model. Moreover, motion detection algorithm and discrimination algorithm between indoor and outdoor environments are developed and could be used to further improve the positioning accuracy of a fingerprint-based positioning system. Tracking algorithm adapted to the fingerprint-based approach is developed and implemented. As result, a robust positioning system is achieved.