A subspace-based direction finding algorithm using fractional lower order statistics

We propose several classes of fractional lower order moment (FLOM)-based matrices that can be used with MUSIC to estimate the DOAs of independent circular signals embedded in additive SαS (symmetric α stable) noise (e.g., sea clutter). We run simulations with different choices of the FLOM parameter p for our FLOM-based matrices and conclude that when the noise is SαS with unknown α≠2, FLOM-multiple signal classification (MUSIC) with p close to unity yields good performance. The performance of FLOM-MUSIC and robust covariation-based (ROC)-MUSIC are similar. Three scenarios that contain circular signals (phase modulation (PM), circularly symmetrical Gaussian, and quaternary phase-shift keying (QPSK)) and one scenario that contains noncircular signals (binary phase-shift keying (BPSK)), all embedded in the same SαS noise, are tested. These simulation results reveal that the scenario containing BPSK signals leads to poor performance, indicating that FLOM-MUSIC is presently limited to circular signals