{"_id":"rvh7NtnWb4jNEzcMr","bibbaseid":"yu-wei-zheng-groupsparselmsformultiplesystemidentification-2015","authorIDs":[],"author_short":["Yu, L.","Wei, C.","Zheng, G."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["L."],"propositions":[],"lastnames":["Yu"],"suffixes":[]},{"firstnames":["C."],"propositions":[],"lastnames":["Wei"],"suffixes":[]},{"firstnames":["G."],"propositions":[],"lastnames":["Zheng"],"suffixes":[]}],"booktitle":"2015 23rd European Signal Processing Conference (EUSIPCO)","title":"Group sparse LMS for multiple system identification","year":"2015","pages":"1691-1695","abstract":"Armed with structures, group sparsity can be exploited to extraordinarily improve the performance of adaptive estimation. In this paper, a group sparse regularized least-mean-square (LMS) algorithm is proposed to cope with the identification problems for multiple/multi-channel systems. In particular, the coefficients of impulse response function for each system are assumed to be sparse. Then, the dependencies between multiple systems are considered, where the coefficients of impulse responses of each system share the same pattern. An iterative online algorithm is proposed via proximal splitting method. At the end, simulations are carried out to verify the superiority of our proposed algorithm to the state-of-the-art algorithms.","keywords":"adaptive estimation;iterative methods;least mean squares methods;signal processing;group sparse LMS;multiple system identification;adaptive estimation;least-mean-square algorithm;impulse response function;iterative online algorithm;proximal splitting method;Signal processing algorithms;Least squares approximations;Convergence;Steady-state;Standards;Correlation;Algorithm design and analysis;LMS;Multiple system identification;Group sparsity;Proximal splitting method","doi":"10.1109/EUSIPCO.2015.7362672","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103383.pdf","bibtex":"@InProceedings{7362672,\n author = {L. Yu and C. Wei and G. Zheng},\n booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},\n title = {Group sparse LMS for multiple system identification},\n year = {2015},\n pages = {1691-1695},\n abstract = {Armed with structures, group sparsity can be exploited to extraordinarily improve the performance of adaptive estimation. In this paper, a group sparse regularized least-mean-square (LMS) algorithm is proposed to cope with the identification problems for multiple/multi-channel systems. In particular, the coefficients of impulse response function for each system are assumed to be sparse. Then, the dependencies between multiple systems are considered, where the coefficients of impulse responses of each system share the same pattern. An iterative online algorithm is proposed via proximal splitting method. At the end, simulations are carried out to verify the superiority of our proposed algorithm to the state-of-the-art algorithms.},\n keywords = {adaptive estimation;iterative methods;least mean squares methods;signal processing;group sparse LMS;multiple system identification;adaptive estimation;least-mean-square algorithm;impulse response function;iterative online algorithm;proximal splitting method;Signal processing algorithms;Least squares approximations;Convergence;Steady-state;Standards;Correlation;Algorithm design and analysis;LMS;Multiple system identification;Group sparsity;Proximal splitting method},\n doi = {10.1109/EUSIPCO.2015.7362672},\n issn = {2076-1465},\n month = {Aug},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103383.pdf},\n}\n\n","author_short":["Yu, L.","Wei, C.","Zheng, G."],"key":"7362672","id":"7362672","bibbaseid":"yu-wei-zheng-groupsparselmsformultiplesystemidentification-2015","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103383.pdf"},"keyword":["adaptive estimation;iterative methods;least mean squares methods;signal processing;group sparse LMS;multiple system identification;adaptive estimation;least-mean-square algorithm;impulse response function;iterative online algorithm;proximal splitting method;Signal processing algorithms;Least squares approximations;Convergence;Steady-state;Standards;Correlation;Algorithm design and analysis;LMS;Multiple system identification;Group sparsity;Proximal splitting method"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2015url.bib","creationDate":"2021-02-13T17:31:52.468Z","downloads":0,"keywords":["adaptive estimation;iterative methods;least mean squares methods;signal processing;group sparse lms;multiple system identification;adaptive estimation;least-mean-square algorithm;impulse response function;iterative online algorithm;proximal splitting method;signal processing algorithms;least squares approximations;convergence;steady-state;standards;correlation;algorithm design and analysis;lms;multiple system identification;group sparsity;proximal splitting method"],"search_terms":["group","sparse","lms","multiple","system","identification","yu","wei","zheng"],"title":"Group sparse LMS for multiple system identification","year":2015,"dataSources":["eov4vbT6mnAiTpKji","knrZsDjSNHWtA9WNT"]}