Digital spectral analysis: with applications
Digital spectral analysis: with applications
Signal processing with alpha-stable distributions and applications
Signal processing with alpha-stable distributions and applications
Advanced Topics in Digital Signal Processing
Advanced Topics in Digital Signal Processing
Fast estimation of the parameters of alpha-stable impulsiveinterference
IEEE Transactions on Signal Processing
Parameter estimation and blind channel identification in impulsivesignal environments
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Robust parameter estimation of a deterministic signal in impulsivenoise
IEEE Transactions on Signal Processing
Probability of resolution of the MUSIC algorithm
IEEE Transactions on Signal Processing
A subspace-based direction finding algorithm using fractional lowerorder statistics
IEEE Transactions on Signal Processing
Estimation of the parameters of sinusoidal signals in non-Gaussian noise
IEEE Transactions on Signal Processing
Strongly concave star-shaped contour characterization by algebra tools
Signal Processing
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In the frequency estimation of sinusoidal signals observed in impulsive noise environments, techniques based on Gaussian noise assumption are unsuccessful. One possible way to find better estimates is to model the noise as an alpha-stable process and to use the fractional lower order statistics (FLOS) of the data to estimate the signal parameters. In this work, we propose a FLOS-based statistical average, the generalized covariation coefficient (GCC). The GCCs of multiple sinusoids for unity moment order in SαS noise attain the same form as the covariance expressions of multiple sinusoids in white Gaussian noise. The subspace-based frequency estimators FLOS-multiple signal classification (MUSIC) and FLOS-Bartlett are applied to the GCC matrix of the data. On the other hand, we show that the multiple sinusoids in SαS noise can also be modeled as a stable autoregressive moving average process approximated by a higher order stable autoregressive (AR) process. Using the GCCs of the data, we obtain FLOS versions of Tufts-Kumaresan (TK) and minimum norm (MN) estimators, which are based on the AR model. The simulation results show that techniques employing lower order statistics are superior to their second-order statistics (SOS)-based counterparts, especially when the noise exhibits a strong impulsive attitude. Among the estimators, FLOS-MUSIC shows a robust performance. It behaves comparably to MUSIC in non-impulsive noise environments, and both in impulsive and non-impulsive high-resolution scenarios. Furthermore, it offers a significant advantage at relatively high levels of impulsive noise contamination for distantly located sinusoidal frequencies.