Pattern Matching Performance Analysis Based on Linear Models with Information Backscattered from RFID Tags

Abstract

Pattern matching localization algorithms are commonly proposed in complex indoor environments. Received signal strength indicator (RSSI) of radio signals backscattered from passive ultra high frequency radio frequency identification (RFID) tags is commonly used as the pattern in matching process. Considering RSSI is severely affected by multipath propagation and noise, etc., more types of patterns will be a better choice for further study. In recent years, phase information is becoming more and more important for RFID-based systems. As for the period ambiguity problem of wrapped phase information, phase difference of arrival (PDOA) of double-frequency signals can be utilized as a type of pattern instead of phase of arrival corresponding to the propagation distance. In this paper, we combine RSSI and PDOA as a brand new type of pattern named RP. In previous study, only localization accuracy is considered to prove effective. It is hard to show the matching performance of the type of pattern with the following process of localization algorithms. Here we take advantage of the linear relationship between pattern Euclidean distance and geographic locations Euclidean distance of tags fixed in reference points to evaluate the distinctiveness performance of RP in matching. The linear correlation of the model is tested at each reference point with experimental data captured from indoor scenario. By evaluating these models statistically, we think RP is better than RSSI and PDOA as a pattern.