STAR-RIS-Assisted Full-Space Angle Estimation via Finite Rate of Innovation

Conventional sensor architectures typically restrict angle estimation to the half-space. By enabling simultaneous transmission and reflection, simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) can support full-space angle detection. This paper develops a fullspace angle estimation framework by leveraging a finite rate of innovation (FRI) model enabled by STAR-RIS. We distinguish two practical STAR-RIS configurations: (i) an element-wise uniform setting, where all metasurface elements share identical energy-splitting (ES) coefficients and phase differences, and (ii) a nonuniform ES setting, where the phase difference is common across elements while the ES coefficients vary element-wise to increase design flexibility. For each regime, we formulate the corresponding FRI-based signal model and derive the Ziv-Zakai bound (ZZB) for angle estimation. To recover the underlying FRI sampling structure, we develop a proximal-gradient algorithm implemented via alternating projections in matrix space and establish its convergence. Exploiting the recovered FRI structure, we construct an annihilating filter whose zeros encode user angles, enabling gridless estimation via polynomial root finding. Numerical results demonstrate that the proposed methods operate reliably across both configuration regimes and achieve improved angle estimation performance with low overhead.