Spectral slope-based approach for mitigating bistatic space-time adaptive processing clutter dispersion

In this study, the effects of bistatic clutter spectral dispersion on space-time adaptive processing (STAP) techniques are considered. A robust spectral slope-based approach is presented for mitigating the bistatic geometry-induced performance loss by pre-processing the secondary data used for covariance estimation. Owing to its capability to compensate both the clutter spectral centres misalignment and the trace slope variability over range, this approach is shown to yield further dispersion reduction with respect to previously proposed strategies, thus improving the performance of STAP. An adaptive version of the proposed approach is also introduced, which is able to extract the required parameters from the same data set used for the covariance matrix estimation, without requiring prior knowledge or ancillary data. The performance analysis against synthetic clutter data shows that the proposed slope-based compensation technique is rather robust with respect to the spectral trace shape and estimation errors, providing equal or better performance with respect to previously derived techniques, depending on the specific bistatic geometry.