{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:48:24Z","timestamp":1760240904664,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,10,26]],"date-time":"2019-10-26T00:00:00Z","timestamp":1572048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The diffusion subband adaptive filtering (DSAF) algorithm has attracted much attention in recent years due to its decorrelation ability for colored input signals. In this paper, a modified DSAF algorithm using the symmetry maximum correntropy criterion (MCC) with individual weighting factors is proposed and discussed to combat impulsive noise, which is denoted as the MCC-DSAF algorithm. During the iterations, the negative exponent in the Gaussian kernel of the MCC-DSAF eliminates the interference of outliers to provide a robust performance in non-Gaussian noise environments. Moreover, in order to enhance the convergence for sparse system identifications, a variant of MCC-DSAF named as improved proportionate MCC-DSAF (MCC-IPDSAF) is presented and investigated, which provides a dynamic gain assignment matrix in the MCC-DSAF to adjust the weighted values of each coefficient. Simulation results verify that the newly presented MCC-DSAF and MCC-IPDSAF algorithms are superior to the popular DSAF algorithms.<\/jats:p>","DOI":"10.3390\/sym11111335","type":"journal-article","created":{"date-parts":[[2019,10,28]],"date-time":"2019-10-28T04:44:31Z","timestamp":1572237871000},"page":"1335","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Diffusion Correntropy Subband Adaptive Filtering (SAF) Algorithm over Distributed Smart Dust Networks"],"prefix":"10.3390","volume":"11","author":[{"given":"Ying","family":"Guo","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China"}]},{"given":"Jingjing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2450-6028","authenticated-orcid":false,"given":"Yingsong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Microwave Remote Sensing, Chinese Academy of Sciences, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1109\/JPROC.2014.2306253","article-title":"Adaptive, networks","volume":"102","author":"Sayed","year":"2014","journal-title":"Procs. 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