Abstract
Research summary
This paper introduces two novel iterative algorithms, namely Polarimetric Sparse Learning via Iterative Minimization (POL-SLIM) and Polarimetric Sparse Iterative Covariance-based Estimation (POL-SPICE), for Direction-of-Arrival (DOA) estimation using a narrowband polarimetric array sensing system. The considered sensor comprises two subarrays of elements with orthogonal polarizations, which collect data in the presence of multiple narrow-band Radio Frequency (RF) signals emitted from unknown angular directions. The proposed algorithms, fast-converging and hyperparameter free, leverage the inherent sparsity of the signal model (across multiple snapshots) to estimate the DOAs. Numerical analysis shows the effectiveness of POL-SLIM and POL-SPICE to accurately estimate the DOA of the sources, in particular with a small number of collected snapshots. Moreover, they attain the Cramér-Rao bound (CRB) at a Signal-to-Noise Ratio (SNR) value lower than the single-polarization counterparts. © 2023 IEEE.