Detection of fading overlapping multipath components

Yousef, N.R.; Sayed, Ali H.; Khajehnouri, N.

doi:10.1016/j.sigpro.2005.11.002

Cited by 15 publications

(11 citation statements)

References 25 publications

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“…J n (e) = E{G(e)} − E{G(e gauss )} (6) where E{·} denotes the statistical expectation. G(·) is the nonlinear function, such as G(e) = e 3 , G(e) = e· exp(−e 2 /2) and G(e) = tanh(c·e) [22].…”

Section: Negentropy Maximizationmentioning

confidence: 99%

“…Sparse signal reconstruction, or compressed sensing, is an emerging field in signal processing and communication [1][2][3][4][5][6]. The problem of recovering a sparse signal from a very low number of linear measurements arises in many real application fields, ranging from error correction and lost data recovery, to image acquisition and reconstruction.…”

Section: Introductionmentioning

confidence: 99%

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Sparse Coding Algorithm with Negentropy and Weighted ℓ1-Norm for Signal Reconstruction

Zhao

Liu

Wang

et al. 2017

Entropy

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Abstract:Compressive sensing theory has attracted widespread attention in recent years and sparse signal reconstruction has been widely used in signal processing and communication. This paper addresses the problem of sparse signal recovery especially with non-Gaussian noise. The main contribution of this paper is the proposal of an algorithm where the negentropy and reweighted schemes represent the core of an approach to the solution of the problem. The signal reconstruction problem is formalized as a constrained minimization problem, where the objective function is the sum of a measurement of error statistical characteristic term, the negentropy, and a sparse regularization term, p -norm, for 0 < p < 1. The p -norm, however, leads to a non-convex optimization problem which is difficult to solve efficiently. Herein we treat the p -norm as a serious of weighted 1 -norms so that the sub-problems become convex. We propose an optimized algorithm that combines forward-backward splitting. The algorithm is fast and succeeds in exactly recovering sparse signals with Gaussian and non-Gaussian noise. Several numerical experiments and comparisons demonstrate the superiority of the proposed algorithm.

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Section: Negentropy Maximizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sparse Coding Algorithm with Negentropy and Weighted ℓ1-Norm for Signal Reconstruction

Zhao

Liu

Wang

et al. 2017

Entropy

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“…We incorporate the gradient of the CIM into the iteration (4) directly, and then the sparse NLMP algorithm can be derived as   (8) where the kernel width  is a free parameter in the penalty term. A proper kernel width will make CIM a good approximationto the -norm constraint [19][20].…”

Section: Sparse Aware Nlmp Algorithmsmentioning

confidence: 99%

“…Based on the assumption of Gaussian noise model, sparsely-aware least mean square(LMS) filtering algorithms(e.g., zero-attracting LMS [1], reweighted zeroattracting LMS [1], sparse regularized 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 such as multipath fading channels estimation, underwater acoustic (UWA) channelprobing as well as echo cancellation [8][9]. It is well known that Gaussian assumption has been broadly accepted because of the Central Limit Theorem (CLT).…”

Section: Introductionmentioning

confidence: 99%

Sparsity aware normalized least mean p-power algorithms with correntropy induced metric penalty

Zhao

et al. 2015

2015 IEEE International Conference on Digital Signal Processing (DSP)

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Abstract-For identifying the non-Gaussian impulsive noise systems, normalized LMP (NLMP) has been proposed to combat impulsive-inducing instability. However, the standard algorithm is without considering the inherent sparse structure distribution of unknown system. 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). 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 can further adjust convergence speed and steady-state error. Numerical simulations are given to confirm the proposed algorithms.Keywords-normalized least mean p-power (NLMP); variable regularized ; correntropy induced metric (CIM);sparse parameter estimation; non-Gaussian impulsive noise.

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“…Such problem arises in many applications such as in multipath fading channels, acoustic channel, geolocation even in simplified models of ultra-wideband communications [1][2][3][4]. In these situations, when considering the characteristics of the channel system, the parameter estimation may have superior performance.…”

Section: Introductionmentioning

confidence: 99%

Sparse least mean p-power algorithms for channel estimation in the presence of impulsive noise

Chen

et al. 2015

SIViP

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The least mean p-power (LMP) is one of the most popular adaptive filtering algorithms. With a proper p value, the LMP can outperform the traditional least mean square ( p = 2), especially under the impulsive noise environments. In sparse channel estimation, the unknown channel may have a sparse impulsive (or frequency) response. In this paper, our goal is to develop new LMP algorithms that can adapt to the underlying sparsity and achieve better performance in impulsive noise environments. Particularly, the correntropy induced metric (CIM) as an excellent approximator of the l 0 -norm can be used as a sparsity penalty term. The proposed sparsity-aware LMP algorithms include the l 1 -norm, reweighted l 1 -norm and CIM penalized LMP algorithms, which are denoted as ZALMP, RZALMP and CIMLMP respectively. The mean and mean square convergence of these algorithms are analysed. Simulation results show that the proposed new algorithms perform well in sparse channel estimation under impulsive noise environments. In particular, the CIMLMP with suitable kernel width will outperform other algorithms significantly due to the superiority of the CIM approximator for the l 0 -norm. B Wentao Ma

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Detection of fading overlapping multipath components

Cited by 15 publications

References 25 publications

Sparse Coding Algorithm with Negentropy and Weighted ℓ1-Norm for Signal Reconstruction

Sparse Coding Algorithm with Negentropy and Weighted ℓ1-Norm for Signal Reconstruction

Sparsity aware normalized least mean p-power algorithms with correntropy induced metric penalty

Sparse least mean p-power algorithms for channel estimation in the presence of impulsive noise

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