Abstract
This work deals with the block-sparse system identification problem on the basis of the maximum correntropy criterion (MCC). The MCC is known for its robustness against non-Gaussian noise and interference in many signal processing applications. With the aim of exploiting the block-sparse property of the system, we introduce a regularization function into the standard cost function of MCC. Based on the modified cost function, an online kernel adapting strategy is developed to further improve the estimation accuracy. Steady-state performance analysis is conducted to explore the behavior of the proposed method. The simulation results illuminate the validity of the theoretical analysis and confirm the superiority of the proposed method in block-sparse system identification through comparisons with state-of-the-art MCC techniques.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
