A computation and memory efficient implementation of STAP for an airborne side-looking radar

Two new methods for implementing full rank Space Time Adaptive Processing (STAP) are introduced in this paper. The motivation behind this work is to implement a full rank STAP algorithm for an airborne side-looking radar which can be easily parallelised and implemented on Graphics Processing Units (GPUs) for near real-time performance. The central idea for both the methods is to use a least squares approach directly on the raw data instead of a covariance matrix to calculate the adaptive filter weights. Both methods are compared in terms of computation cost, resource requirements and target detection performance using MCARM dataset with well established methods such as Sample Matrix Inversion (SMI) STAP and the Hung-Turner method.