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Two-Dimensional Processing for Radar Systems

Abstract : This report presents algorithms and methodologies for space-time adaptive processing (STAP) and detection in airborne surveillance phased array radar systems. Two approaches are considered for the know-signal case: weight vectors for multichannel one-dimensional (MC1D) data representations, and parametric models for single-channel (scalar) two-dimensional (SC2D) data representations. For the MC1D approach, a generic architecture that covers a wide variety of weight vector algorithms and associated detection rules is presented. Algorithms covered include the classical matched filter (MF) and three new methods which are low-dimensionality alternatives to the MF. The new methods can be configured in a variety of ways based on the selection of a handful of key parameters. The SC2D parametric approach is a novel extension of the parametric adaptive matched filter (PAMF) pioneered by Rangaswamy and Michels (1997). In this context the radar channel output data is represented as a scalar 2-D system, and parametric 2-D models are utilized to represent the channel output data under the target-absent hypothesis. Robust linear model identification algorithms are applied to estimate the 2-D model parameters. All algorithms introduced herein admit adaptive (data-based) formulations, and offer reduced computational requirements in relation to the classical MF. Future work includes performance and computational load assessments in relation to other methods.