National Key Research and Development Program of the China-Government Corporation Special Program [2016YFE0111100]
; Science and Technology innovative Talents Foundation of Harbin [2016RAXXJ044]
; Projects for the Selected Returned Overseas Chinese Scholars of Heilongjiang Province
; MOHRSS of China
; PhD Student Research and Innovation Fund of the Fundamental Research Funds for the Central Universities [HEUGIP201707]
英文摘要:
A general zero attraction (GZA) proportionate normalized maximum correntropy criterion (GZA-PNMCC) algorithm is devised and presented on the basis of the proportionate-type adaptive filter techniques and zero attracting theory to highly improve the sparse system estimation behavior of the classical MCC algorithm within the framework of the sparse system identifications. The newly-developed GZA-PNMCC algorithm is carried out by introducing a parameter adjusting function into the cost function of the typical proportionate normalized maximum correntropy criterion (PNMCC) to create a zero attraction term. The developed optimization framework unifies the derivation of the zero attraction-based PNMCC algorithms. The developed GZA-PNMCC algorithm further exploits the impulsive response sparsity in comparison with the proportionate-type-based NMCC algorithm due to the GZA zero attraction. The superior performance of the GZA-PNMCC algorithm for estimating a sparse system in a non-Gaussian noise environment is proven by simulations.
