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A novel multilayer neural network model for TOA-based localization in wireless sensor networks

Sayed Yousef Monir Vaghefi ; Sayed Reza Monir Vaghefi (Institutionen för signaler och system, Kommunikationssystem)
2011 International Joint Conference on Neural Network, IJCNN 2011; San Jose, CA; 31 July 2011 through 5 August 2011 p. 3079-3084 . (2011)
[Konferensbidrag, refereegranskat]

A novel multilayer neural network model, called artificial synaptic network, was designed and implemented for single sensor localization with time-of-arrival (TOA) measurements. In the TOA localization problem, the location of a source sensor is estimated based on its distance from a number of anchor sensors. The measured distance values are noisy and the estimator should be able to handle different amounts of noise. Three neural network models: the proposed artificial synaptic network, a multi-layer perceptron network, and a generalized radial basis functions network were applied to the TOA localization problem. The performance of the models was compared with one another. The efficiency of the models was calculated based on the memory cost. The study result shows that the proposed artificial synaptic network has the lowest RMS error and highest efficiency. The robustness of the artificial synaptic network was compared with that of the least square (LS) method and the weighted least square (WLS) method. The Cramer-Rao lower bound (CRLB) of TOA localization was used as a benchmark. The model's robustness in high noise is better than the WLS method and remarkably close to the CRLB.



Denna post skapades 2011-11-23. Senast ändrad 2016-10-26.
CPL Pubid: 148923

 

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Institutioner (Chalmers)

Institutionen för signaler och system, Kommunikationssystem

Ämnesområden

Elektroteknik och elektronik

Chalmers infrastruktur