RSS-Based DoA Estimation for ESPAR Antennas Using Support Vector Machine

Abstract

In this letter, it is shown how direction-of-arrival (DoA) estimation for electronically steerable parasitic array radiator (ESPAR) antennas, which are designed to be integrated within wireless sensor network nodes, can be improved by applying support vector classification approach to received signal strength (RSS) values recorded at an antenna's output port. The proposed method relies on ESPAR antenna's radiation patterns measured during the initial calibration phase of the DoA estimation process. These patterns are then used in the support vector machine (SVM) training process adapted to handle ESPAR antenna-based DoA estimation. Measurements using a fabricated ESPAR antenna indicate that the proposed SVM approach provides more accurate results than available RSS-based estimation algorithms relying on power pattern cross-correlation method.