Virtual Spherical Modes for AoA Estimation With Small Subwavelength Antennas

Abstract

Phased arrays are pivotal for determining the angle of arrival (AoA) of signals. However, they are impractical for size-constrained and weight-constrained platforms. Recently developed spherical modes (SM) multiport antennas offer a compact beamforming solution due to their ability to generate multiple radiating modes within a confined volume. This work exploits this methodology by introducing a sparse sensing (SS) technique to generate additional virtual SM, increasing the degrees of freedom (DoF) of AoA detection. It is demonstrated that SS with MUltiple SIgnal Classification (MUSIC) and estimation of signal parameters via rotational invariance technique algorithms can identify up to six uncorrelated sources with a five-port SM beamforming antenna, resulting in the number of simultaneously detected sources being greater than the number of ports used. The technique allows for increased accuracy, computational efficiency, and DoF for AoA estimation, highlighting its potential for Internet of Things applications.