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Artificial Neural Network Based Model for Local Position Systems


Syahrulanuar Ngah, Hui Zhu, Kui-Ting Chen, Yuji Tanabe, Takaaki Baba


Vol. 9  No. 7  pp. 97-106


In this paper an Artificial Neural Network (ANN) based model is introduced to improve the positioning accuracy in local environment. With the learning ability to deal with unknown environment, the proposed model can be used to convert received Time of Arrive (TOA) signals into corresponding positions. Supported by the good and enough training data (related data to solve the problem), the ANN based model can provide better positioning accuracy and precision, compared with previous positioning algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Moreover, the computation time used by ANN based model is reduced to an extremely short value. Comprehensive performance comparisons including accuracy, precision and computation time are presented in this paper. Results described in this paper demonstrate that the proposed model produce a high accuracy position information. The computation time used by ANN based model is only 10% and 20% of what GA and PSO used, respectively. The proposed model can be successfully used in local positioning with high-quality solution.


Local Positioning, Neural Network Based Model