LAUSR

A new form of space-time adaptive processing (STAP) is presented that leverages additional training data obtained from waveform-diverse pulse compression filters possessing low cross-correlation with the primary waveform that is used for traditional airborne and space-based ground moving target indication. In contrast to traditional training data in which clutter and targets are “focused” in range via pulse compression of the primary waveform, this new set of training data possesses a “smeared” range response that better approximates the identically distributed assumption made during sample covariance estimation. The Multi-Waveform STAP (MuW-STAP or simply $\mu $-STAP) formulation is shown for both multiple-input multiple-output and single-input multiple-output configurations, with the former retaining the spatially-focused primary emission supplemented by low-power secondary emissions that illuminate sidelobe clutter and the latter a special case of the former. In simulation, signal to interference plus noise ratio analysis reveals enhanced robustness to nonstationary interference compared to standard STAP training data.