Signal processing with alpha-stable distributions and applications
Signal processing with alpha-stable distributions and applications
Time series: data analysis and theory
Time series: data analysis and theory
Broadband Beamforming with Alpha-Stable Distributions
ASILOMAR '95 Proceedings of the 29th Asilomar Conference on Signals, Systems and Computers (2-Volume Set)
Array signal processing using higher-order and fractional lower-order statistics
Array signal processing using higher-order and fractional lower-order statistics
Fast estimation of the parameters of alpha-stable impulsiveinterference
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Symmetric alpha-stable filter theory
IEEE Transactions on Signal Processing
High-resolution direction finding: the missing data case
IEEE Transactions on Signal Processing
Probability of resolution of the MUSIC algorithm
IEEE Transactions on Signal Processing
Data block adaptive filtering algorithms for α-stable random processes
Digital Signal Processing
Adaptive blind equalization for MIMO systems under α-stable noise environment
IEEE Communications Letters
Space-time blind equalization under α-stable noise environment
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Modulation Recognition of MFSK Signals Based on Multifractal Spectrum
Wireless Personal Communications: An International Journal
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The class of alpha-stable distributions is better for modeling impulsive noise than Gaussian distribution in array signal processing. This paper briefly introduces the statistical characteristics of stable distribution and its fractional lower-order statistics, covariation and fractional-order correlation, and proposes a new minimum norm method (FOC-MinNorm) of direction finding based on the fractional-order correlation and subspace technique under alpha-stable noise conditions. We analyze the performances of the FOC-MinNorm, including the accuracy in the estimation, the capability and probability of resolution, and the pseudo peaks of the FOC-MinNorm method. The analysis is based on the assumption that the additive noise can be modeled as a complex alpha-stable process. Simulation and analysis show that the proposed method is robust in a wide range of characteristic exponent values of stable distribution. Its resolution capability and probability of resolution are better than those of the conventional second-order statistics based MinNorm algorithm and covariation based ROC-MUSIC method, furthermore, fractional-order correlation is more suitable than covariation in practical applications.