A multi-waveform version of space-time adaptive processing denoted as MuW-STAP (or simply μ-STAP) was recently developed that incorporates the training data generated by secondary waveform/filter pairs into the estimation of the sample covariance matrix. This additional training data was found to improve robustness to heterogeneous clutter. Here SINR analysis is used to evaluate the μ-STAP approach under various clutter conditions and with multiple additional sets of training data obtained through the use of multiple different pulse compression filters applied to the same received data.
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