Structured covariance estimation for space-time adaptive processing

Adaptive algorithms require a good estimate of the interference covariance matrix. In situations with limited sample support such an estimate is not available unless there is structure to be exploited. In applications such as space-time adaptive processing (STAP) the underlying covariance matrix is structured (e.g., block Toeplitz), and it is possible to exploit this structure to arrive at improved covariance estimates. Several structured covariance estimators have been proposed for this purpose. The e cacy of several of these are analyzed in this paper in the context of a variety of STAP algorithms. The SINR losses resulting from the di erent methods are compared. An example illustrating the superior performance resulting from a new maximum likelihood algorithm (based upon the expectationmaximization algorithm) is demonstrated using simulation and experimental data.