10.1 Introduction

Aerospace radar systems must detect a variety of target types in the presence of severe, dynamic clutter and jamming signals. Signal diversity -the exploitation of azimuthal, elevation, Doppler, range and polarisation measurement spaces - is a necessary component of advanced detection architectures. Space-time adaptive processing ^[1] (STAP) improves the detection of slow moving and/or low radar cross section (RCS) targets competing with mainlobe and sidelobe ground clutter returns [1]. Additionally, in light of the equivalence between the maximum signal-to-interference-plus-noise ratio (SINR) filter and the minimum variance beamformer, we recognise STAP as a member of the class of superresolution algorithms [2]. For this reason, STAP is a key element of radar systems whose electrically small apertures, and hence relatively large beamwidths, would otherwise seriously affect clutter-limited detection performance.

Adaptive filters adjust their response in accord with estimates of interference characteristics. A critical distinction exists between optimal and adaptive filters. Specifically, the optimum filter design requires clairvoyant knowledge of interference statistics (e.g. known covariance matrix), while the adaptive implementation relies on necessarily imperfect estimates of unknown interference parameters. A training stage generates estimates of these unknown parameters. Hence, STAP is a data-domain implementation of the optimum filter. The optimum filter defines the upper bound on STAP's detection performance potential.

In the multivariate Gaussian case, maximising SINR equivalently maximises the probability of detection ( P _D) for a fixed probability of false alarm ( P _FA) [1].

Consider an N-channel array receiving M