STAP adaptive weight training using phase and power selection criteria

This paper addresses the issue of adaptive weight training in space-time adaptive processing (STAP) algorithms for airborne radar in non-homogeneous clutter environments, while avoiding the inclusion of target signals in the training. A common method of ensuring STAP clutter cancellation performance in the presence of strong clutter discretes is to train the STAP adaptive weights using the returns with the largest power for a given Doppler bin. The presumption is that the strongest returns are from the strongest clutter. Many times, however, targets are also present whose inclusion in the STAP weight training results in significant target self-nulling as well as a degradation in clutter mitigation performance. A new STAP training method is presented that excises targets from the training set based on an interferometric measurement of phase for each potential STAP training sample. The resulting training method based on both phase and power selection criteria is shown to offer significant performance gains on experimental data.