Automatic Configuration-Parameters Estimation and Range-Dependence Compensation Using Simulated, Single-Realization, Random Snapshots in STAP

摘要

Mitigation of radar ground clutter via STAP requires estimating the interference-plus-noise snapshot covariance matrix at each range. The typical approach is to average single-realization sample covariance matrices at neighboring ranges. However in most configurations snapshots have range- dependent spectral characteristics leading to severe performance degradations. In 1 we proposed range-dependence compensation methods based on registering the clutter ridges using a mathematical theory of direction-Doppler (DD) curves. We considered the cases where configuration parameters are either known or unknown. However performance was examined using only true theoretical covariance matrices derived from analytical clutter models. Here, we extend the work in 1 by using simulated random realization of snapshots instead of true, theoretical covariance. The extension is straightforward for known configurations. It becomes a challenge for unknown configuration Here, we must estimate the unknown configuration parameters from single realizations of random snapshots at selected neighboring ranges. We have developed a new, multiprong algorithm that solves a series of simplex optimization problems, each tailored to a specific class of monostatic and bistatic configurations. Once the parameters are found, range compensation proceeds as in 1. The overall performance of the new method is quantified by its SINR loss and compared to those of earlier methods. For omnidirectional antennas, the method reliably estimates the configuration parameters. For directive antennas, estimation becomes less accurate, but performance still appears better than that of other published methods. A sample support size analysis reveals that reasonable performance is achievable with just a few neighboring snapshots. Moreover, in this case, the loss in the clutter-free area of the SINR loss plot is approximately zero.