Statistical analysis of multi-channel detection using data from airborne AESA radar

We investigate the ground clutter homogeneity and target detection performance using airborne multi-channel AESA (Active Electronically Scanned Array) radar data from flight trials over the southern part of Sweden. The data sets consisted of clutter returns from both urban and rural areas. Multivariate normality tests indicate a significant difference between these two environments, and particularly the urban clutter does not fit the Gaussian signal model. Furthermore, we expose differences in terms of homogeneity as well. As homogeneity measure we employed the commonly used generalized inner product (GIP), and compared this to a homogeneity measure based on projection in the eigenspace of the covariance matrix. The focus of our interest was to examine the importance of screening the secondary data from non-homogeneities, when using adaptive target detection in AESA radar systems. To evaluate the non-homogeneity detection (NHD) performance we employed a synthetic target scheme. Our evaluation showed negligible improvement from NHD preprocessing, regardless of method, and hence we conclude that the clutter in our data sets is structurally homogeneous. However, in dense target scenarios it is crucial to screen for target contamination in secondary data, as we demonstrate on real data.