Reduced-Dimension STAP Method via Beamforming Joint Subarray Synthesis for Space-Based Early Warning Radar

Space-based early warning radar (SBEWR) offers advantages such as extended detection distances and more flexible deployment options compared to airborne early warning radar (AEWR). However, range dependence or nonstationarity of clutter becomes more complex in SBEWR. Theoretically, the traditional 3-D space-time adaptive processing (3D-STAP) method can effectively suppress nonstationary clutter. Nonetheless, the substantial computational demands and extensive requirements of training samples make real-time processing of the full-dimension 3D-STAP method impractical. In this article, we analyze the complex coupling relationship of clutter in SBEWR and further develop a reduced-dimension 3D-STAP method. The proposed method combines beamforming and subarray synthesis, where the former is employed to mitigate clutter densely distributed in the azimuth dimension, and the latter is utilized to suppress clutter continuously varied in the elevation-Doppler domain. This tailor-made reduction structure can effectively decouple the clutter of SBEWR in the azimuth-elevation–Doppler domain, demonstrating superior performance compared to other reduced-dimension 3D-STAP methods. In comparison to the full-dimension 3D-STAP method, the proposed method significantly reduces computational complexity and sample requirements. Furthermore, extensive experimental results demonstrate the superiority of the proposed method regarding signal-to-clutter-plus-noise ratio loss, minimum detectable velocity (MDV), and target detection performance.