An adaptive combination constrained proportionate normalized maximum correntropy criterion algorithm for sparse channel estimations

Abstract An adaptive combination constrained proportionate normalized maximum correntropy criterion (ACC-PNMCC) algorithm is proposed for sparse multi-path channel estimation under mixed Gaussian noise environment. The developed ACC-PNMCC algorithm is implemented by incorporating an adaptive combination function into the cost function of the proportionate normalized maximum correntropy criterion (PNMCC) algorithm to create a new penalty on the filter coefficients according to the devised threshold, which is based on the proportionate-type adaptive filter techniques and compressive sensing (CS) concept. The derivation of the proposed ACC-PNMCC algorithm is mathematically presented, and various simulation experiments have been carried out to investigate the performance of the proposed ACC-PNMCC algorithm. The experimental results show that our ACC-PNMCC algorithm outperforms the PNMCC and sparse PNMCC algorithms for sparse multi-path channel estimation applications