A switched-order FLOM STAP algorihm in heterogeneous clutter environment

The normalized fractionally-lower order moment (NFLOM) algorithm exhibits fast convergence but low steady- state signal-to-interference-plus noise ratio (SINR) when the order is less than two. In this paper, we propose a switched-order NFLOM algorithm to adaptively select the best order to achieve both fast convergence and good steady-state performance. The basic idea is to constrain the order within a range of appropriate values, to compute the space-time adaptive processing (STAP) the best order that maximizes the output SINR. The proposed algorithm is assessed with simulated data considering a heterogeneous clutter environment. The simulation results illustrate that our proposed algorithm outperforms the normalized least mean squares (NLMS) algorithm and the NFLOM algorithm, and has an easier parameter setting than the existing variable-order algorithms.