Research on chipless radio frequency identification (RFID) systems are mainly emphasizing on improving the RFID reader architecture and the chipless tag design. As a result, tag detection techniques are overshadowed, hence they are using primitive signal processing techniques. The main focus of this chapter is to improve the signal processing techniques using the same reader architecture and tag design. Therefore, the proposed tag detection techniques are compatible with the existing RFID systems. The improvements are expected in successful tag detection rate and the tag reading range.
The existing chipless signal processing techniques for tag detection is as primitive as threshold-based detection. Maximum likelihood (ML)-based detection techniques have shown improved performances in communication systems over primitive techniques such as threshold-based detection techniques. The motivation for this work is to apply the ML detection techniques for chipless RFID tag detection so that the existing RFID systems would produce better results in terms of the detection error rate (DER) and the reading range.
The rest of the chapter is organized as follows. First, the theory behind deriving four ML expressions for a single-input single-output (SISO)-based chipless RFID system is presented. The different expressions are derived based on the availability of the channel information (known or unknown channel) ...