Multi-tag radio-frequency identification systems based on new blind source separation neural networks

Abstract Electronic systems are progressively replacing mechanical devices or human operation for identifying people or objects in everyday-life applications. Especially, the radio-frequency contactless identification systems available today have several advantages, but they cannot handle easily several simultaneously present items. This paper describes a solution to this problem, based on blind source separation techniques. The effectiveness of this approach is experimentally demonstrated, using workstation and real-time DSP-based implementations of the proposed system. More precisely, various source separation neural networks are compared, and the networks that we proposed recently are shown to be the most attractive ones, thanks to their simplicity, good performance and self-normalized (i.e. “automated”) operation.

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