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Error Gradient-based Variable-Lp Norm Constraint LMS Algorithm for Sparse System Identification
[article]
2015
arXiv
pre-print
Preserved Fulltext
To address this problem, we propose a novel variable p-norm constraint least mean square (LMS) algorithm, which serves as a variant of the conventional Lp-LMS algorithm established for sparse system identification ...
However, when applied for system identification, most priori work in sparse norm constraint adaptive filtering suffers from the difficulty of adaptability to the sparsity of the systems to be identified ...
of sparse norm constraint adaptive filtering suffer from the difficulty of adaptability to the sparsity of system, the present work in this paper develops a novel Lp norm constraint LMS algorithm coined ...
arXiv:1509.07951v1
fatcat:3rvoarhci5gdrbay3nx3p7dxuq
Variable p norm constrained LMS algorithm based on gradient of root relative deviation.pdf
[article]
2016
arXiv
pre-print
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A new Lp-norm constraint least mean square (Lp-LMS) algorithm with new strategy of varying p is presented, which is applied to system identification in this letter. ...
of sparse system identification in the presence of noise. ...
Variable p norm constrained LMS algorithm based on gradient of root relative deviation
Yong Feng ✉ , Fei Chen and Jiasong Wu A new Lp-norm constraint least mean square (Lp-LMS) algorithm with new strategy ...
arXiv:1603.09022v1
fatcat:wqq3gnnukvabzgfttnxnuot5ju
Sparsity Aware Normalized Least Mean p-power Algorithms with Correntropy Induced Metric Penalty
[article]
2015
arXiv
pre-print
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Based on the first proposed algorithm, moreover, we propose an improved CIM constraint variable regularized NLMP(CIMVRNLMP) algorithm by utilizing variable regularized parameter(VRP) selection method which ...
To exploit sparsity as well as to mitigate the impulsive noise, this paper proposes a sparse NLMP algorithm, i.e., Correntropy Induced Metric (CIM) constraint based NLMP (CIMNLMP). ...
least square (RLS) [2] , -norm constrained LMS (L0-LMS) [3] , -norm constrained LMS(LP-LMS) [4] and its variations [5] [6] [7] ) have been developed and also applied successfully in many applications ...
arXiv:1503.00792v1
fatcat:xqb3un76lbdvvf5u5vx52eszb4
A Sparsity-Aware Variable Kernel Width Proportionate Affine Projection Algorithm for Identifying Sparse Systems
2019
Symmetry
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A sparsity-aware variable kernel width proportionate affine projection ...
The developed LP-VPAP is realized by developing the variable kernel width technique and the l p -norm-like constraint. ...
The Developed LP-VPAP Algorithm Herein, we detailedly analyze the developed LP-VPAP algorithm, which is implemented by the variable kernel width technique and the l p -norm-like constraint to devise a ...
doi:10.3390/sym11101218
fatcat:vbw5wqqpf5exnbriynsqdnauqa
A Class of Diffusion Zero Attracting Stochastic Gradient Algorithms with Exponentiated Error Cost Functions
2019
IEEE Access
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For sparse system identification in the adaptive network, a polynomial variable scaling factor improved diffusion least sum of exponentials (PZA-VSIDLSE) algorithm and an l p -norm constraint diffusion ...
Distributed estimation algorithms based on the popular mean-square error criterion have poor behavior for sparse system identification with color noise. ...
Simulations are implemented to verify the performances of the LP-DLE2 and PZA-VSIDLSE algorithms for the sparse system identification. ...
doi:10.1109/access.2019.2961162
fatcat:dzruc2mnavcvji6c2bfs2tnpga
Sparsity aware normalized least mean p-power algorithms with correntropy induced metric penalty
2015
2015 IEEE International Conference on Digital Signal Processing (DSP)
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The second one is an improved CIM constraint variable regularized NLMP (CIMVRNLMP) algorithm, in which variable regularized parameter (VRP) is selected to adjust convergence speed and steady-state error ...
However, the standard algorithm is developed without considering the inherent sparse structure distribution of unknown system. ...
) [1] , sparse regularized least square (RLS) [2] , 0 l -norm constrained LMS (L0-LMS) [3] , p l -norm constrained LMS(LP-LMS) [4] and its variations [5] [6] [7] ) have been developed and also applied ...
doi:10.1109/icdsp.2015.7251952
dblp:conf/icdsp/MaQZCG15
fatcat:xy66sdebqbe5ven4mhoejgvxee
Two Are Better Than One: Adaptive Sparse System Identification Using Affine Combination of Two Sparse Adaptive Filters
2014
2014 IEEE 79th Vehicular Technology Conference (VTC Spring)
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One of popular adaptive sparse system identification (ASSI) methods is adopting only one sparse least mean square (LMS) filter. ...
Sparse system identification problems often exist in many applications, such as echo interference cancellation, sparse channel estimation, and adaptive beamforming. ...
Koichi Adachi of Institute for Infocomm Research for his valuable comments and suggestions. ...
doi:10.1109/vtcspring.2014.7023132
dblp:conf/vtc/GuiKMA14
fatcat:qdh5kzlx6zdntid7jyrhutco3y
Two are Better Than One: Adaptive Sparse System Identification using Affine Combination of Two Sparse Adaptive Filters
[article]
2013
arXiv
pre-print
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One of popular adaptive sparse system identification (ASSI) methods is adopting only one sparse least mean square (LMS) filter. ...
Sparse system identification problems often exist in many applications, such as echo interference cancellation, sparse channel estimation, and adaptive beamforming. ...
ACKNOWLEDGMENT This work was supported by grant-in-aid for the Japan Society for the Pro motion of Science (JSPS) fellows grant number 24 02366. ...
arXiv:1311.1312v1
fatcat:ferj4ofzxzhuxn3f7s3luapulq
Underwater Acoustic Channel Adaptive Estimation using l21 Norms
[article]
2021
arXiv
pre-print
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The problem of underwater acoustic (UWA) channel estimation is the non-uniform sparse representation that may increase the algorithm complexity and the required time. ...
Furthermore, it can achieve a better performance in terms of mean square error (MSE) and execution time. ...
Mei, "l f0g norm constraint lms algorithm for sparse system identification," IEEE Signal Processing Letters, vol. 16, no. 9, pp. 774-777, 2009 . References 1 - 1 A. S. Gupta and G. R. ...
arXiv:2108.13189v1
fatcat:ygokuty4qvfyfgcf3umvrbzowu
An Improved Proportionate Normalized Least-Mean-Square Algorithm for Broadband Multipath Channel Estimation
2014
The Scientific World Journal
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Our simulation results demonstrate that the proposed algorithm can effectively improve the estimation performance of the PNLMS-based algorithm for sparse channel estimation applications. ...
To make use of the sparsity property of broadband multipath wireless communication channels, we mathematically propose anlp-norm-constrained proportionate normalized least-mean-square (LP-PNLMS) sparse ...
echo cancellation and system identification, which are known as the zero-attracting LMS (ZA-LMS) and reweighted ZA-LMS (RZA-LMS) algorithms, respectively [15] . ...
doi:10.1155/2014/572969
pmid:24782663
pmcid:PMC3981014
fatcat:zc3udzyljfe33la6zp6fri7s24
A Stochastic Gradient Approach on Compressive Sensing Signal Reconstruction Based on Adaptive Filtering Framework
2010
IEEE Journal on Selected Topics in Signal Processing
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2013 pre-print arXiv:1303.2257v1 fatcat:taavccu7ajdn3afwqomet2azbi
This approach employs a stochastic gradient-based adaptive filtering framework, which is commonly used in system identification, to solve the sparse signal reconstruction problem. ...
Based on the methodological similarity between sparse signal reconstruction and system identification, a new approach for sparse signal reconstruction in compressive sensing (CS) is proposed in this paper ...
Detao Mao at the University of British Columbia for his part in improving the English expression of this paper. ...
doi:10.1109/jstsp.2009.2039173
fatcat:6kfjlois6vdgfokyk3edbr6iri
Reweighted l1-norm Penalized LMS for Sparse Channel Estimation and Its Analysis
[article]
2014
arXiv
pre-print
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A new reweighted l1-norm penalized least mean square (LMS) algorithm for sparse channel estimation is proposed and studied in this paper. ...
An expression for the excess mean square error (MSE) of the algorithm is also derived which suggests that under the right conditions, the reweighted l1-norm penalized LMS algorithm outperforms the standard ...
In [13] , the idea of using a weighted l 1 -norm penalty for the purpose of sparse system identification is presented without any convergence analysis. ...
arXiv:1401.3566v1
fatcat:yijvimxbf5haxf2t2gnvhudefm
Reweighted l1-norm penalized LMS for sparse channel estimation and its analysis
2014
Signal Processing
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A new reweighted l 1 Ànorm penalized least mean square (LMS) algorithm for sparse channel estimation is proposed and studied in this paper. ...
An expression for the excess mean square error (MSE) of the algorithm is also derived which suggests that under the right conditions, the reweighted l 1 Ànorm penalized LMS algorithm outperforms the standard ...
In [13] , the idea of using a weighted l 1 Ànorm penalty for the purpose of sparse system identification is presented without any convergence analysis. ...
doi:10.1016/j.sigpro.2014.03.048
fatcat:s4iledbvifhxvb3u3nsfc2tbcy
Efficient Parameters Estimation Method for the Separable Nonlinear Least Squares Problem
2020
Complexity
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In this work, we combine the special structure of the separable nonlinear least squares problem with a variable projection algorithm based on singular value decomposition to separate linear and nonlinear ...
Then, we propose finding the nonlinear parameters using the Levenberg–Marquart (LM) algorithm and either solve the linear parameters using the least squares method directly or by using an iteration method ...
Solution of Parameters for Separable Nonlinear Least Squares
Variable Projection Algorithm Based on SVD. ...
doi:10.1155/2020/9619427
fatcat:ychefgh5lbcydk64svmgllak7u
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1808.05403v2 fatcat:wxjzwd67cvc2jig5feczrlmuqaOther Versions
algorithms. ...
This paper gives an overview of this topic in various fields in signal processing, statistics and machine learning, including compressive sensing (CS), sparse regression and variable selection, sparse ...
Least-Mean-Square (LMS) Filter: For sparse system identification, regularized least-mean-square (LMS) algorithms have shown advantage over traditional LMS algorithms, e.g., be more accurate, more efficient ...
arXiv:1808.05403v3
fatcat:lfq3t5gvgngmllu27ml7xnehtm
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