Complementary Fusion of Motion-Model and Data-Extrapolation Strategies for Improved Angle Estimation in Shipborne HFSWR

Mobile radar platforms offer high manoeuvrability but suffer from limited array apertures that degrade angle estimation performance. Existing virtual aperture extension methods for shipborne high-frequency surface wave radar (HFSWR) fall into two categories: motion-model-based approaches utilising space-time equivalence and extrapolation-based approaches operating on range-Doppler (RD) domain data blocks. Each has inherent limitations—the former constrains aperture size by platform motion distance, whereas the latter limits aperture size by cumulative noise or estimation errors in extrapolation. To address these complementary drawbacks, this paper proposes a complementary fusion of motion-model and data-extrapolation strategies (CFMDS) for improved angle estimation. The CFMDS method fuses both strategies within a unified processing framework. The motion-model-based component handles scenarios with sufficient motion, whereas the extrapolation-based component addresses cases where extrapolation errors are manageable. By leveraging the strengths of both approaches, the method achieves larger effective aperture and more robust angle estimation than either approach alone. Simulation and experimental results validate the effectiveness and superiority of the proposed method.