Multi-window post-Doppler dimensionality reduction for multi-waveform STAP

Abstract

A multi-waveform version of space-time adaptive processing, denoted as MuW-STAP (or W-STAP), was recently developed as a single-input multiple-output (SIMO) emission scheme that incorporates training data generated by multiple secondary filters into the estimation of the sample covariance matrix. This integration of additional training data was found to increase robustness to non-homogeneous clutter because the secondary filters serve to "homogenize" the interference in range. Here we incorporate μ-STAP into multi-window post-Doppler STAP (specifically PRI-Staggered and Adjacent-Bin implementations) to assess the impact when dimensionality reduction techniques are employed. SINR analysis was used to evaluate the performance of these reduced dimension μ-STAP formulations under various simulated clutter conditions.