A novel fast high-resolution SSMUSIC algorithm based on short data samples

In this paper, we propose a novel fast high-resolution SSMUSIC (Signal Subspace Scaled MUSIC) algorithm which focuses on short data samples based on MSWF (Multi-Stage Wiener Filter) theory. The method we propose, which we call MSWF SSMUSIC, is superior to MUSIC in angular resolution (separation) and angular estimation performance, particularly in the low signal-to-noise ratio (SNR) and/or small measurement support regimes, and has lower computational complexity than SSMUSIC algorithm with slighter performance loss. The proposed approach is quite suitable for short data lengths or when the environment is quite dynamic, with the addition of satisfactory performance and relatively reduced computational load. Numerical results are presented to illustrate the effectiveness and validity of the proposed method.