Two-Step Low-Complexity Space-Time Adaptive Processing (STAP)

This work proposes a low-complexity space-time adaptive processing (STAP) algorithm for sensing applications built on a moving platform in the presence of strong clutters. The proposed algorithm achieves low-complexity computation via two steps. First, it utilizes improved fast approximated power iteration methods to compress the data into a much smaller subspace. To further reduce the computational complexity, a progressive singular value decomposition (SVD) approach is employed to update the inverse of the covariance matrix of the compressed data. As a result, the proposed low-complexity STAP algorithm can achieve order-of-magnitude computational complexity reduction as compared to conventional STAP algorithms. Simulation results are shown to confirm the validity of the proposed algorithm.