Effect of varying the step-size of least mean squares filter in the accuracy of extraction of passive RFID root-music direction-of-arrival estimates

Abstract

This work establishes the extraction of root-multiple signal classification (root-MUSIC) direction-of-arrival (DoA) estimates of a passive radio frequency identification (RFID) tag system with a reader that utilizes a two-element uniform linear array. The accuracy of the estimates can be improved by using an adaptive filter called least mean squares algorithm (LMS) to reduce the effect of noise and carrier leakage before the extraction is done. Through the use of a simulation, random complex signals are primarily set from angle bearings of -90 through positive 90 degrees, inclusive of carrier leakage and noise, characterized as additive, white, random, and Gaussian-distributed. The LMS filter, with step sizes of 0.008, 0.003 and 0.002, is designed to detect the deterioration of affected parameters of the complex signal in order to reduce the inaccuracy of the estimates as effects of the added distortion. The accuracy of the estimates are compared to the actual DoA of the tag by measuring the error in degrees and with respect to the variation of the step-size. Simulations have also been done to observe the effect of signal-to-noise ratio (SNR) of the received signal and the increase of the number of samples taken before extraction, in addition to the variation of the step size of the filter.