Joint off-grid parameters estimation based on sparse reconstruction for polarization-sensitive arrays

Sparse reconstruction techniques including off-grid processing are widely applied in direction-of-arrival (DOA) estimation. However, multi-dimensional grid support is required in multi-dimensional estimation scenarios, which is computationally expensive and difficult to implement. In this work, a three-dimensional (3D) DOA and polarization joint estimation problem is studied and a decoupled off-grid signal model is first established, which decomposes the 3D estimation problem into two separate processes: DOA estimation and polarization parameters estimation. Then, an off-grid algorithm is proposed to estimate DOA based on a dynamic dictionary strategy, in which the algorithm no longer uses the fixed grids but dynamically updates the grid points to achieve DOA estimation in the continuous domain. After that, a structure-constrained method is provided to further estimate the polarization parameters, which provides a closed-form solution without requiring additional estimation pairing. Simulation results demonstrate that the proposed algorithm outperforms existing methods under various challenging conditions, including scenarios with a small number of snapshots, low signal-to-noise ratios, and the presence of coherent signals.