Polarimetric Space–Time Adaptive Processing in Airborne FDA Radar

Polarimetric space–time adaptive processing (PSTAP) introduces polarization domain information alongside the space–time domain. By exploiting the differences in polarimetric scattering characteristics between targets and clutter, PSTAP significantly improves the detection performance for slow-moving targets and has become an important technology in the airborne radar community. However, the Conventional polarimetric STAP still faces the severe range ambiguity problem caused by high pulse repetition frequency (PRF) in high-speed scenarios. To overcome range ambiguity, this paper introduces the frequency diverse array (FDA) into polarimetric STAP and develops an FDA-PSTAP framework. First, we establish the fundamental signal processing procedure for FDA-PSTAP. Second, the full-dimensional (FD) FDA-PSTAP is developed according to the MVDR principle. Finally, to reduce the sample requirement for training samples, a reduced-dimensional (RD) FDA-PSTAP is proposed by designing a proper reduced-dimension transformation matrix. Numerical results demonstrate that the proposed FDA-PSTAP effectively suppresses range-ambiguous clutter. Under limited training samples, the proposed reduced-dimensional FDA-PSTAP reduces the output SINR loss by 15–17 dB compared with the full-dimensional counterpart in the main clutter region. When the number of training samples is doubled, the proposed method approaches the optimal performance with a gap of 1–2 dB across the entire Doppler spectrum, confirming its robustness and practical applicability.