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IEEE Signal Processing Letters, ISSN 1070-9908, 12/2016, Volume 23, Issue 12, pp. 1786 - 1790
Journal Article
IEEE Transactions on Circuits and Systems II: Express Briefs, ISSN 1549-7747, 03/2016, Volume 63, Issue 3, pp. 274 - 278
Journal Article
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, ISSN 1520-6149, 04/2009, pp. 3125 - 3128
We propose a new approach to adaptive system identification when the system model is sparse. The approach applies lscr 1 relaxation, common in compressive... 
Adaptive systems | Filtering | Steady-state | Least squares approximation | Convergence | Filters | LMS | Mean square error methods | Cost function | System identification | Acceleration | sparse models | compressive sensing | zero-attracting | l1 norm relaxation | Compressive sensing | L1 norm relaxation | Sparse models | Zero-attracting
Conference Proceeding
Signal, Image and Video Processing, ISSN 1863-1703, 3/2017, Volume 11, Issue 3, pp. 533 - 540
Journal Article
Signal Processing, ISSN 0165-1684, 12/2010, Volume 90, Issue 12, pp. 3289 - 3293
The zero-attracting LMS (ZA-LMS) algorithm is one of the recently published sparse LMS algorithms. It usesan l -norm penalty in the standard LMS cost function.... 
Sparsity | Least mean square (LMS) algorithm | Mean square deviation (MSD) | Zero attracting | norm
Journal Article
by Shi, K and Shi, P
SIGNAL PROCESSING, ISSN 0165-1684, 12/2010, Volume 90, Issue 12, pp. 3289 - 3293
The zero-attracting LMS (ZA-LMS) algorithm is one of the recently published sparse LMS algorithms. It usesan l(1)-norm penalty in the standard LMS cost... 
l norm | Sparsity | Zero attracting | Mean square deviation (MSD) | Least mean square (LMS) algorithm | ENGINEERING, ELECTRICAL & ELECTRONIC
Journal Article
Signal Processing, ISSN 0165-1684, 11/2016, Volume 128, pp. 243 - 251
A type of norm-adaption penalized least mean square/fourth (NA-LMS/F) algorithm is proposed for sparse channel estimation applications. The proposed NA-LMS/F... 
Least mean square/fourth | Adaptive filter | Sparse channel estimation | Zero attracting | Norm-adaption penalty | CONVERGENCE | P-NORM | CONSTRAINT LMS ALGORITHM | ENGINEERING, ELECTRICAL & ELECTRONIC | RNA | Algorithms | Computer simulation | Least mean squares | Least mean squares algorithm | Norms | Signal processing | Channels | Convergence
Journal Article
International Journal of Communication Systems, ISSN 1074-5351, 05/2017, Volume 30, Issue 8, pp. np - n/a
Summary Sparse least‐mean mixed‐norm (LMMN) algorithms are developed to improve the estimation performance for sparse channel estimation applications. Both the... 
sparse channel estimation | zero attracting | LMS | adaptive filter | LMF | least‐mean mixed‐norm | least-mean mixed-norm | TELECOMMUNICATIONS | ENGINEERING, ELECTRICAL & ELECTRONIC | Algorithms | Broadband | Least mean squares | Mathematical analysis | Adaptive filters | Least mean squares algorithm | Channels | Convergence
Journal Article
AEUE - International Journal of Electronics and Communications, ISSN 1434-8411, 07/2016, Volume 70, Issue 7, pp. 895 - 902
In this paper, we propose a type of sparsity-aware set-membership normalized least mean square (SM-NLMS) algorithm for sparse channel estimation and echo... 
Normalized least mean square | Set-member filtering | Channel estimation | Compressed sensing | Zero attracting | PERFORMANCE | NORMALIZED LMS ALGORITHM | TELECOMMUNICATIONS | ENGINEERING, ELECTRICAL & ELECTRONIC | Algorithms | Computer simulation | Computer-generated environments
Journal Article
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ISSN 1520-6149, 04/2018, Volume 2018-, pp. 4294 - 4298
Group zero-attracting LMS and its reweighted form have been proposed for addressing system identification problems with structural group sparsity in the... 
Simulation | Estimation | Sparse system identification | adaptive parameter adjustment | Mathematical model | Indexes | Matrix decomposition | Covariance matrices | Transient analysis | group sparsity | zero-attracting LMS | Group sparsity | Adaptive parameter adjustment | Zero-attracting LMS
Conference Proceeding
Signal Processing, ISSN 0165-1684, 02/2018, Volume 143, pp. 1 - 6
•This paper is the first approach combining the BP-NLMS and the BZA-NLMS.•We propose a block activeness indicator for block wise convex combination.•We propose... 
Proportionate matrix | Adaptive filter | Zero-attracting penalty | Echo cancellation | NLMS algorithm | IDENTIFICATION | CONSTRAINT LMS ALGORITHM | ENGINEERING, ELECTRICAL & ELECTRONIC | Algorithms | Benzoic acid | Electrical engineering
Journal Article
IEEE Access, ISSN 2169-3536, 2019, Volume 7, pp. 135770 - 135779
Journal Article
Signal Processing, ISSN 0165-1684, 04/2014, Volume 97, pp. 100 - 109
Journal Article
Circuits, Systems, and Signal Processing, ISSN 0278-081X, 5/2016, Volume 35, Issue 5, pp. 1611 - 1624
Journal Article
EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, 12/2018, Volume 2018, Issue 1, pp. 1 - 13
Journal Article
2018 IEEE Radar Conference (RadarConf18), 04/2018, pp. 0809 - 0814
In this paper, the L 1 LMS algorithm is considered as an alternative to conventional cross-correlation processing for generating an estimate of a radar range... 
Simulation | Gaussian noise | Government | non-quadratic LMS | Radar | Cost function | Convex functions | Mathematical model | zero-attracting LMS
Conference Proceeding
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