Identifying surface acoustic wave ID-tags using the total least squares-estimating signal parameters via the rotational invariance technique algorithm

Abstract

Tags based on surface acoustic wave techniques have a great amount of potential for application in radio frequency identification because of their characteristics of wireless sensing and completely passive operation. Since time domain sampling requires high-speed radio frequency switches in the transmitter and fast data acquisition in the receiver, frequency domain sampling (FDS) is used for measurement and frequency-stepped continuous wave is adopted as the interrogation signal. FDS requires, however, the traditional inverse fast Fourier transform method which has a resolution restriction and decreases the identification probability. To identify the tag reliably with higher probability, a high-resolution algorithm is required. In actual measurement, the echo signal attenuates greatly as the distance between the transceiver and the tag increases. Furthermore, the perturbations in the environment also dampen the echo signal. To increase the identification range and allow the system work in harsher environments, it is necessary to enhance the identification capability at low signal noise ratio (SNR). The application of total least squares-estimating signal parameters via the rotational invariance technique is presented. The experimental results show that an excellent identification capability at low SNR is achieved.