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APES based STAP for target detection in spread-Doppler clutter

Nonstationary spread-Doppler clutter (SDC) within a coherent processing interval often precludes target detection. Across range bins, nonstationarity severely limits the amount of training data available to estimate the clutter covariance matrix required for adaptive detection. Such difficult clutter conditions are not uncommon in High Frequency (HF) hybrid sky-surface wave radar which utilizes the ionosphere as propagation media. This paper applies amplitude and phase estimator (APES) based space-time adaptive processing (STAP), which operates only on the primary data, to reject SDC and to make detection decision. A subspace signal model is assumed to describe the potential target perturbed by ionospheric phase path contamination. According to the simulation and experimental results, the proposed method can achieve better detection performance with single dataset.