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Research Paper | Electronics & Communication Engineering | India | Volume 4 Issue 10, October 2015

GPS/RFID Integration Using Feed Forward Time Delayed Neural Networks

Suhas Kshirsagar | Sunita S. Shinde ^[2]

Abstract: A vehicle-mounted GPS receiver used for positioning can undergo from signal blockage. To cure this problem, GPS/ RFID integration can be considered as an answer in some cases. Though, in the case of metropolitan areas where there are severe multipath conditions, the performance degrades noticeably. The last decade we have seen several proposals of techniques aiming at improving the precision of GPS positions. The difficulty of these techniques increases with the increase of the accurateness required. These techniques usually combine multiple techniques like fuzzy logic etc. In this paper, we propose a new technique based just on neural network, which offers a improved performance while showing a lower complexity. The idea is to utilize a neural network, which emulates the behavior of a given estimator in order to change it. Here, we present simulations results, which confirm the performance and the robustness of our proposed scheme in a heterogeneous environment.

Keywords: Kalman Filter, Localization, GPS, RFID, Neural Network, FFTDN

Edition: Volume 4 Issue 10, October 2015,

Pages: 1016 - 1020

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