Performance and computational trades for RD-STAP algorithms in challenging detection environments

Abstract

Reducing the size, weight, and power (SWAP) of airborne radar systems improves efficiency and decreases operational cost. Platform SWAP requirements are directly related to the complexity of algorithms used to process the received signal. Several reduced dimension space-time adaptive processing (RD-STAP) algorithms, which significantly decrease computations over traditional STAP, have been shown to minimally impact GMTI performance in homogeneous clutter. In both heterogeneous clutter and dense target environments, training with data whose characteristics match those of the cell under test may be required to maintain acceptable performance; however, selecting appropriate training data requires additional computations that increase SWAP. This paper identifies and quantifies trades between performance and computations for several RD-STAP algorithms and training methods in challenging signal environments.