Subspace-based frequency estimation of sinusoidal signals in alpha-stable noise

In the frequency estimation of sinusoidal signals observed in impulsive noise environments,tecronmen based on Gaussian noise assumption are unsucI#f#I;G One possible way to find better estimates is to model the noise as an alpha-stable procha and to use the fracIR;f# lower orderstatistic (FLOS) of the data to estimate the signal parameters. In this work, we propose a FLOS-based statisticf average, the generalized crians27i( cfcans2 (GCC). The GCCs ofmultiple sinusoids for unity moment order in S#S noise attain the same form as the cfI;;3Of# expressions of multiple sinusoids in white Gaussian noise. The subspacf#;LRL frequenc estimators FLOS--multiple signal cnalfI#G##f# (MUSIC) and FLOS-- Bartlett are applied to the GCC matrix ofthe data. On the other hand, we show that the multiple sinusoids in S#S noiseci also be modeled as a stable autoregressive moving average procge approximated by a higher order stable autoregressive (AR) proc#GO Using the GCCs ofthe data, we obtain FLOS versions ofTufts--Kumaresan (TK) and minimum norm (MN) estimators, which are based on the AR model. The simulation results show thattecI##;#f employing lower order statistic are superior to their secf3O;##f# statistic (SOS)-based cOS)-basedf; especbase when the noise exhibits a strong impulsive attitude. Among the estimators, FLOS--MUSIC shows a robust performancf It behaves cavesfG33 to MUSIC in non-impulsive noise environments, and both in impulsive and non-impulsive high-resolution sch-resol Furthermore, it offers a significant advantage at relatively high levels of impulsive noise contamination for distantly located sinusoidal frequencies