Hybrid direct data domain sigma-delta space–time adaptive processing algorithm in non-homogeneous clutter

Abstract

The need to deal with non-homogeneous clutter has driven much of the recent research in space–time adaptive processing (STAP). An extension of the low-complexity, sigma-delta (ΣΔ) algorithm incorporating the direct data domain (D3) processing is presented. The new algorithm is practical and improves target detection in non-homogeneous clutter environments. The algorithm employs a hybrid approach, combining D3 processing with the more traditional statistical approach, thereby obtaining advantages of both. First, a modified D3 algorithm, which maximises signal-to-interference-plus-noise ratio (SINR), is presented. Then this D3 algorithm is used as an adaptive transformer to create sum (Σ) and difference (Δ) beams. The residual interference after the D3 processing is further cancelled by ΣΔ STAP. The proposed hybrid algorithm using D3-ΣΔ STAP is tested in non-homogeneous clutter modelled using spherically invariant random variables (SIRV) and artificially injected discrete interferers. Performance of the proposed methods is compared with those of traditional statistical approaches, illustrating significant benefits of hybrid processing in non-homogeneous scenarios.