Abstract
In this paper, by combining the block sparse memory improved proportionate affine projection sign algorithm (BS-MIP- APSA) and the generalized correntropy induced metric (GCIM), we have proposed two block sparse adaptive filtering algorithms, named as GCIM-BS-MIP-APSA-I and GCIM-BS-MIP-APSA-II, by directly using GCIM to measure the sparse information and treating GCIM as a function of block weight vector, respectively, for identification of sparse systems. Based on the good characteristic of GCIM approximating the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell_{0}$</tex> -norm, the proposed algorithms, especially GCIM-BS-MIP-APSA-II, can achieve improved filtering accuracy and faster convergence rate when the identified system is block sparse. We also have analyzed the computational complexity of proposed algorithms. Some simulation results are conducted to demonstrate the efficiency of GCIM-BS-MIP-APSAs compared with other competing algorithms.
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.
