COMPOSITE NORM PROPORTIONATE NORMALIZED MINIMUM ERROR ENTROPY ALGORITHM FOR CLUMP SPARSE CHANNEL ESTIMATION

This paper proposes a composite-norm Proportionate Normalized Minimum Error Entropy (CN-PNMEE) algorithm for clump sparse channel estimation. The proposed algorithm imposes a hybrid -norm onto channel coefficients to contemplate the clump sparse feature of the channel. The proposed CN-PNMEE algorithms is developed and studied in detail. Further, the simulations are carried out to prove the efficacy of the proposed algorithm. The exploratory results show that the developed algorithm is superior to existing normalized minimum error entropy (NMEE), Proportionate Normalized Minimum Error Entropy (PNMEE), zero attracting minimum error entropy (ZA-MEE) and residual zero attracting minimum error entropy (RZA-MEE) algorithms for clump sparse channel in the presence of heavy tailed impulsive observation noise