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

We propose several classes of fractional lower order moment (FLOM)-based ma trices that can be used with MUSIC to estimate the DOAs of independent circ ular signals embedded in additive S alphaS noise (e.g., sea clutter). We ra n simulations with different choices of FLOM parameter p for our FLOM-based matrices and conclude that when the noise is S alphaS with unknown alpha n ot equal 2, FLOM-multiple signal clasification (MUSIC) with p close to unit y yields good performance. The performance of FLOM-MUSIC and robust covaria tion-based (ROC)-MUSIC are similar. Three scenarios that contain circular s ignals [phase modulation (PM), circularly symmetrical Gaussian, and quatern ary phase-shift keying (QPSK)] and one scenario that contains noncircular s ignals [binary phase-shift keying (BPSK)I, all embedded in the same S alpha S noise, are tested, These simulation results reveal that the scenario cont aining BPSK signals leads to poor performance, indicating that FLOM-MUSIC i s presently limited to circular signals.