Signals of opportunity (SoO) enable emission-free passive sensing, but low Earth orbit (LEO) satellite illumination with unmanned aerial vehicle (UAV) array receivers exhibits rapid geometry variation. As a result, the received phase evolves in a space–time coupled manner, and the array snapshots become nonstationary even within one coherent processing interval (CPI), degrading conventional stationary-snapshot direction-of-arrival (DOA) estimators. This paper proposes a decomposition-based sparse reconstruction with successive interference cancellation (D-SR-SIC) framework for dynamic DOA estimation in LEO SoO UAV passive sensing. The proposed estimator leverages a sparse-reconstruction signal model and is implemented via a computationally efficient decomposition-based search-and-cancel procedure. A short-CPI parameterized space–time phase model captures the common motion-induced phase history and the time-varying steering drift; the coupled multi-parameter estimation is decomposed into two low-dimensional correlation searches followed by least-squares amplitude estimation and multi-target peeling. Optional local refinement and multi-beam pre-screening improve robustness to off-grid mismatch, near–far interference, and wide field-of-view operation. Simulations show that the proposed method achieves about 0.11∘ DOA root-mean-square error (RMSE) at −20 dB signal-to-noise ratio (SNR) in a representative highly dynamic setting.
Liu et al. (Sun,) studied this question.