Smart tag detection techniques for chipless RFID systems

Radio Frequency Identification (RFID) is a wireless technology used to automatically identify objects attached to its tags. Its applications span in different areas such as inventory control, logistics, security and item tracking. Vast majority of commercially available RFID tags use Application-Specific Integrated Circuits (ASICs) to encode and transmit data. This micro-chip in the RFID tag makes the tag manufacturing process complicated and expensive compared to optical barcode printing. Researchers have brought the idea of removing the micro-chip and using chipless techniques to encode data into tags, allowing them to be passive, printable and low cost. However, chipless RFID technologies have still not been able to replace relatively expensive chipped RFID tags mainly due to less tag bit capacity. Over the last decade, researchers have mainly focused on improving the chipless RFID tag design and the RFID reader architecture. However, they were mostly using primitive signal processing techniques such as moving average or threshold based detection. The few advanced signal processing techniques reported so far have high computation complexity, hence not feasible for commercial implementation. This thesis presents smart tag detection techniques that are computationally feasible and allowing high tag data encoding capacity. Firstly, four different maximum likelihood (ML) based tag detection techniques have been developed based on the reader architecture and channel knowledge. In addition, all of them are able to operate based on both the time and frequency domain data samples of any frequency domain tag. One of the detection techniques jointly detects the channel as well as the tag type without having any prior channel knowledge or a calibration tag. A fifth tag detection technique was developed for an existing frequency domain tag reader using the magnitude of the tag response. However, these single input single output (SISO) based tag detection techniques suffer from high computation complexity. Two new detection methods have been developed using the likelihood expressions derived in above techniques to reduce the computation complexity from exponential to linear order. The first method was a suboptimal bit by bit detection technique (serial reading) and the second method is a fully optimal Trellis tree based Viterbi decoding technique. Then a novel, multiple input multiple output (MIMO) based chipless RFID system was introduced and a tag detection technique for the proposed system was developed. Finally a MIMO chipless tag was designed which includes a broadband equal power divider, monopole antennas and spiral resonators. It was found that, the proposed tag detection techniques for SISO systems provides significantly higher tag reading accuracy over the existing threshold based detector. In addition, they are capable of operating without a guard-band which makes the tag data bit capacity to be doubled without compromising the reading accuracy. Moreover, the effective SNR gain provided by the proposed techniques can be represented as increasing the tag reading range. All these benefits were achieved without compromising the low computation complexity. The MIMO tag with 2 branches is capable of encoding up to 4 times the total bits stored in existing SISO tags. These smart tag detection techniques are expected to increase the data bit capacity in chipless RFID tags hence produce commercialized chipless RFID systems in future.