Exploiting the Persymmetric Property of Covariance Matrices for Knowledge-Aided Space-Time Adaptive Processing

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 impr...

Separable approximations of space-time covariance matrices

Statistical modeling of space-time data has often been based on separable covariance functions, that is, covariances that can be written as a product of a purely spatial covariance and a purely temporal covariance. The main reason is that the structure of separable covariances dramatically reduces the number of parameters in the covariance matrix and thus facilitates computational procedures fo...

Knowledge-Based Control For Space Time Adaptive Processing

Space-time adaptive processing

Space-Time Adaptive Processing: Fundamentals

In this lecture, we present the principles of space-time adaptive processing (STAP) for radar, applied to moving target indication. We discuss the properties of optimum STAP, as well as problems associated with estimating the adaptive weights not encountered with spatial-only processing (i.e. beamforming).