Secondary Data Support and Non-Homogeneities in Space-Time Adaptive Processing

One of the primary problems with the application of Space-Time Adaptive Processing (STAP) techniques is secondary data support for the interference plus noise covariance matrix estimate. Reed has shown the required secondary data support to achieve performance within 3 dB of optimal SINR is approximately equal to twice the degrees of freedom (DOF) used in the algorithm. Reed proved this rule for Sample Matrix Inversion (SMI) techniques. A concern arises when applying this rule to a newer class of reduced dimension STAP algorithms that do not fall under the SMI umbrella. This thesis focuses on the Cross Spectral Metric (CSM) algorithm developed by Goldstein and Reed. Through Monte Carlo simulations, the thesis proves Reed's rule for sample support is not accurate in this case. Optimum SINR performance for the CSM algorithm was obtained by choosing the number of DOF in the algorithm equal to the dimension of the interference subspace. With this choice, the required sample support for the covariance matrix estimate is 2.5 times the DOF used in the algorithm. This relationship is only true when the number of DOF is equal to the interference subspace dimension. A second goal of the thesis determines the impact of non- homogeneities within the secondary data on the CSM algorithm. The Generalized Inner Product (GIP) detection scheme is then used to excise these non- homogeneities from the secondary data. The CSM algorithm was found to be susceptible to non-homogeneities. The use of the GIP successfully negated the impact on this algorithm.Air Force Inst of Tech Wright-Patterson AFB OH School of Engineering is the author of 'Secondary Data Support and Non-Homogeneities in Space-Time Adaptive Processing', published 1997 under ISBN 9781423567233 and ISBN 1423567234.