Robust adaptive filtering algorithms for α-stable random processes
Copyright policy )Robust adaptive filtering algorithms for α-stable random processes
The paper studies robust adaptive filtering algorithms in an impulsive noise environment. In particular, the case of additive finite mean noise modeled by the so called \(\alpha\)-stable processes is investigated, and algorithms based on fractional low order statistics are proposed. The performance of the resulting adaptive filters is compared to those of the traditional normalized least mean square and normalized least mean \(p\)-norm algorithms.
- Bilkent University Turkey
Alpha stable random processes, Impulsive Signals, Statistical methods, Normalized least mean p norm, Adaptive filtering, Least mean square algorithms, Alpha-stable Random Processes, Spurious signal noise, 519, impulsive noise, Probability density function, adaptive filtering, Finite impulse response adaptive filtering, Adaptive Filtering, fractional low order statistics, Mathematical models, Random processes, Adaptive algorithms, Lms Algorithm, Filtering in stochastic control theory, FIR filters, Impulsive signals, \(\alpha\)-stable random processes, LMS algorithms, Stability
Alpha stable random processes, Impulsive Signals, Statistical methods, Normalized least mean p norm, Adaptive filtering, Least mean square algorithms, Alpha-stable Random Processes, Spurious signal noise, 519, impulsive noise, Probability density function, adaptive filtering, Finite impulse response adaptive filtering, Adaptive Filtering, fractional low order statistics, Mathematical models, Random processes, Adaptive algorithms, Lms Algorithm, Filtering in stochastic control theory, FIR filters, Impulsive signals, \(\alpha\)-stable random processes, LMS algorithms, Stability
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