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• We firstly give an analysis solution of the proximal operator of the L1 over Linf in compressed sensing. • We construct a novel variant of fast iterative shrinkage-thresholding algorithm (FISTA) based on the Moreau envelop of the L1/Linf. • We design a specific and convergent variable-splitting scheme of the alternating direction method of multipliers (ADMM) by introducing a simple auxiliary variable. In this paper, we study minimization of the ratio of ℓ 1 and ℓ ∞ norms (ℓ 1 / ℓ ∞) as a nonconvex and sparsity-promoting metric for solving the unconstrained compressed sensing problems. To design some efficient algorithms, we derive a closed-form solution to the proximal operator of the ℓ 1 / ℓ ∞ function. With the newly variant of the shrinkage operator, we propose a novel variant of fast iterative shrinkage-thresholding algorithm (FISTA) and a specific variable-splitting scheme of the alternating direction method of multipliers (ADMM) which is guaranteed to have sub-sequential convergence. Experimentally, we construct extensive numerical simulations to demonstrate the efficiency of these two proposed approaches over the state-of-the-art algorithms in sparse recovery. [ABSTRACT FROM AUTHOR]

Highlights • The Tobit Kalman filtering problem is studied for discrete time-varying systems. • Non-Gaussian Levy and time-correlated additive measurement noises are considered. • The measurement differencing method and Levy-Ito theorem are utilized. • A modified recursive filtering algorithm is designed where the unknown noise covariances are carefully calculated. • Simulation results are provided to illustrate the effectiveness of the proposed filter. Abstract The Tobit Kalman filter (TKF) is a powerful tool in solving the state estimation problem for linear systems with censored measurements. This paper is concerned with the Tobit Kalman filtering problem for discrete time-varying systems subject to non-Gaussian Lévy and time-correlated additive measurement noises. By referencing to the measurement differencing method, the time-correlation of the measurement noises is transformed into the cross-correlation between the equivalent measurement noise and the process noise. Then, by resorting to the Lévy-Ito theorem, the non-Gaussian Lévy measurement noises are transformed into equivalent Gaussian noises with unknown covariances. Based on the transformed Gaussian measurement noises, a modified recursive TKF is designed where the unknown noise covariances are carefully calculated. Simulation results are provided to illustrate the effectiveness of the proposed filter. [ABSTRACT FROM AUTHOR]

When coping with the large outliers in measurement caused by the non-Gaussian environmental noise, although the MCC criterion adopts the high-order statistics, the residual error for large outliers still exists. Considering that the M-estimate works well in minimum square error criterion and it can truncate the outliers and further improve the robustness, in this paper, we propose the robust kernel recursive least squares algorithms and the robust kernel recursive maximum correntropy algorithms based on three M-estimate methods. Then, numerical simulations verify that the M-estimates help the proposed algorithms have better performance than the conventional kernel recursive adaptive filtering against the non-Gaussian noise. [ABSTRACT FROM AUTHOR]

In recent years face hallucination or super-resolution (SR) is getting much attention due to its wide applicability in real world scenarios. The existing SR methods and models perform well for noise free or small camera/atmospheric noisy faces. However, when suffering from mixed Impulse-Gaussian (MIG) noise, face hallucination becomes a challenging task. To address this problem, a novel error shrunk nearest neighbors representation (ESNNR) based face hallucination algorithm is proposed in this paper. Here, local content prior is incorporated to identify the high variance content (HVC) in the input images. The proposed algorithm suppresses the identified HVC in the input face to minimize the squared error. Moreover, the similarity matching between the input and training images is improved to achieve the locality and sparsity in the presence of MIG noise. Simulation results performed on public FEI, CAS-PEAL, CMU+MIT face databases, and locally captured surveillance video frames show that the proposed algorithm is computationally efficient, suitable for practical applications and give better performance than the existing face SR methods. [ABSTRACT FROM AUTHOR]

Parameter estimation is important in signal modeling and signal filtering. This paper uses the filtering technique to study the parameter estimation problems of a class of bilinear systems, for which the input-output representation is derived by eliminating the state variables in the systems. According to the obtained identification model, a two-stage least squares based iterative algorithm and a filtering based least squares iterative algorithm are proposed for estimating the parameters of bilinear systems with colored noises by using the hierarchical identification principle and the data filtering technique. The least squares based iterative algorithm is given for comparison. The simulation results show that the proposed algorithms have good performance in estimating the parameters of bilinear systems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a reduced-complexity algorithm to estimate the direction of arrivals (DOAs) of multiple uncorrelated narrow-band signals. We show that with an array of arbitrary configuration, the real part of the array covariance matrix can be equivalently reformulated as an entire array covariance matrix received by a virtual array with potential signal model available for DOA estimate. We provide in-depth insights into the eigenvalue decomposition (EVD) on the real part of the array covariance matrix, based on which we propose a novel virtual array transformation multiple signal classification (VAT-MUSIC) algorithm for DOA estimation with efficient real-valued computations. The new algorithm is able to reduce about 75% computational complexity and it has easy implementation advantages over state-of-the-art real-valued techniques. Furthermore, the performance of the proposed algorithm is theoretically analyzed and a closed-form expression is derived to predict the mean square error (MSE) of the new DOA estimator. Numerical simulations are conducted to demonstrate the advantages of the proposed algorithm and to verify the theoretical analysis. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a global Gaussian mixture (GM) reduction algorithm via clustering for extended target tracking in clutter. The proposed global clustering algorithm is obtained by combining a fuzzy Adaptive Resonance Theory (ART) neural network architecture with the weighted Kullback–Leibler (KL) difference which describes discrimination of one component from another. Therefore, we call the proposed algorithm as ART-KL clustering (ART-KL-C) in the paper. The weighted KL difference is used as a category choice function of ART-KL-C, derived by considering both the KL divergence between two components and their weights. The performance of ART-KL-C is evaluated by the normalized integrated squared distance (NISD) measure, which describes the deviation between the original and reduced GM. The proposed algorithm is tested on both one-dimensional and four-dimensional simulation examples, and the results show that the proposed algorithm can more accurately approximate the original mixture and is useful in extended target tracking. [Copyright &y& Elsevier]

Population Monte Carlo (PMC) sampling methods are powerful tools for approximating distributions of static unknowns given a set of observations. These methods are iterative in nature: at each step they generate samples from a proposal distribution and assign them weights according to the importance sampling principle. Critical issues in applying PMC methods are the choice of the generating functions for the samples and the avoidance of the sample degeneracy. In this paper, we propose three new schemes that considerably improve the performance of the original PMC formulation by allowing for better exploration of the space of unknowns and by selecting more adequately the surviving samples. A theoretical analysis is performed, proving the superiority of the novel schemes in terms of variance of the associated estimators and preservation of the sample diversity. Furthermore, we show that they outperform other state of the art algorithms (both in terms of mean square error and robustness w.r.t. initialization) through extensive numerical simulations. [ABSTRACT FROM AUTHOR]

This paper concerns message passing based approaches to sparse Bayesian learning (SBL) with a linear model corrupted by additive white Gaussian noise with unknown variance. With the conventional factor graph, mean field (MF) message passing based algorithms have been proposed in the literature. In this work, instead of using the conventional factor graph, we modify the factor graph by adding some extra hard constraints (the graph looks like being ‘stretched’), which enables the use of combined belief propagation (BP) and MF message passing. We then propose a low complexity BP-MF SBL algorithm based on which an approximate BP-MF SBL algorithm is also developed to further reduce the complexity. Thanks to the use of BP, the BP-MF SBL algorithms show their merits compared with state-of-the-art MF SBL algorithms: they deliver even better performance with much lower complexity compared with the vector-form MF SBL algorithm and they significantly outperform the scalar-form MF SBL algorithm with similar complexity. [ABSTRACT FROM AUTHOR]

The paper introduces a new method for Blind Source Separation (BSS) in noisy instantaneous mixtures of both independent or dependent source component signals. This approach is based on the minimization of a regularized criterion. Precisely, it consists in combining the total variation method for denoising with the Kullback–Leibler divergence between copula densities. The latter takes advantage of the copula to model the structure of the dependence between signal components. The obtained algorithm achieves separation in a noisy context where standard BSS methods fail. The efficiency and robustness of the proposed approach are illustrated by numerical simulations. [ABSTRACT FROM AUTHOR]

• To analyze the performance of three proportionate algorithms in impulsive noises. • To reveal the ranges and relations of the step-sizes in three proportionate algorithms. • To develop a decorrelation technique based variable step-size scheme to deal with the trade-off between convergence rate and steady-state error. In the literature, the proportionate least mean M-estimate (PLMM) algorithm exhibits good performance when dealing with sparse systems in the presence of impulsive noises. In this paper, the mean and mean-square behaviors of three recursion types of the PLMM algorithm are studied in depth. We derive analytically the stability, transient and steady-state results of these PLMM recursions, and find that they can achieve the same performance when properly choosing the step-size and the proportionate matrix. To improve the filter performance in both convergence rate and steady-state error, we derive a variable step-size (VSS) scheme and then present the VSS-based PLMM algorithms. In addition, based on the adaptive decorrelation strategy aiming at the colored input signals, the VSS adaptive decorrelation PLMM algorithms are developed to further speed up the convergence. Computer simulations have verified our theoretical analyses and the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Abstract: A multistatic passive radar system exploits illuminators of opportunity like radio or television transmitters for target surveillance. In this paper we analyze the use of the illumination of television or radio stations broadcasting digital signals (DAB/DVB-T) from a target tracking view point. An important characteristic of this type of illuminators is the joint operation of several illuminators in a single frequency network (SFN). As a consequence it is a priori unknown which measurement belongs to which illuminator. This property expands the typical association task of associating measurements with targets by the task of associating measurements with illuminators. In this paper we present a comprehensive concept for target tracking in DAB/DVB-T networks accounting for the characteristics of this signal. The derived algorithms will be tested with respect to numerical simulations including a sensitivity study for parameter mismatch between simulations and tracking model. [Copyright &y& Elsevier]

Abstract: Track correlation is a fundamental problem in distributed multitarget multisensor tracking system. It entails selecting the most probable association between target tracks from a very large set of possibilities. First, the inherent disadvantages of the present two kinds of track correlation algorithms are pointed out in this paper. Then a novel track correlation algorithm based on the gray theory is presented and the detailed steps are described. This method can weigh with the degree of relative correlation through the developing trend between the factors in data arrays, which breaks through the limitations of the sample quantities and the typical distributing rule. Effect of discrimination coefficient is also discussed. At last, the correct correlation rate and computational time cost of the proposed algorithm are compared with weighted algorithm and statistical double threshold algorithm through simulation. The simulation results show that the performance of gray track correlation algorithm proposed in this paper is much better than that of the classical methods under the environments of dense targets, interfering, noise, tracks cross and so on. [Copyright &y& Elsevier]

Abstract: Based on a generalized Mexican-hat function proposed, the paper presents an algorithm for the design of a new class of continuous wavelets matched to arbitrary transient signals. While the generalized Mexican-hat wavelets are constructed from the standard Mexican-hat function by utilizing a polynomial series with the admissible condition imposed, the signal matching characteristics are achieved by minimizing the spectral differences between the reference signal and the generalized Mexican-hat wavelet via even and odd symmetry decomposition. Detail derivations are given to yield a set of linear simultaneous equations for determination of the appropriate polynomial coefficient values for the generalized Mexican-hat wavelet, thereby shaping its waveform to resemble the reference transient signal. The solution for the equations is shown to be given by a combination of analytical expressions and numerical approximation. Also included in the paper are computer simulations and practical examples to demonstrate the signal matching quality and the influence of various design parameters. Comparisons are made with the standard Mexican-hat wavelet and correlation methods to reveal the superior performance of the generalized and matched Mexican-hat wavelet, in terms of enhancement of signal features in the time-scale domain, and reliable detection of transient signals and their variants embedded in noise. [Copyright &y& Elsevier]

• The major contribution of this paper is to proposed a PSA detection method without the knowledge of large-scale fading factors, which makes it more practical in real world applications. • The BS performs the channel estimation after receiving pilots sent from users. Then the BS transmits dedicated signals constructed with the channel estimation results back to users, where the final decision is made. Specifically, the dedicated signals are divided into two parts, where the first part helps estimate the large-scale fading coefficients, while the second is used to design the decision metric. • We derive the mean and variance of the proposed decision metric, based on which the PSA detection method is then presented. Performance analysis concerning the probabilities of false alarm (P f a) and detection (P d) is carried out. Moreover, the relationship between the detection performance and key system parameters are investigated. In massive MIMO systems, the training phase provides opportunity for an eavesdropper to attack legitimate communications, which is known as pilot spoofing attack (PSA). This paper proposes an algorithm to detect the PSA based on the transmission of dedicated signals. In contrast to existing methods, the proposed scheme doesn't rely on a prior knowledge of large-scale factors. Specifically, the user sends pilots to the base station, where the channel estimation is performed. After that, dedicated signals are constructed using the channel estimation result and then sent back to users. These signals are divided into two parts, where the first part helps acquire the large-scale fading factors and the second one is exploited for the PSA detection. In addition, performance analysis concerning the probability of detection is presented, particularly the impact of key parameters on it. Finally, numerical simulations are carried out to validate the proposed PSA detection method. [ABSTRACT FROM AUTHOR]

Augmented adaptive filters provide superior performance over their conventional counterparts when dealing with noncircular complex signals. However, the performance of such filters may change considerably when they are implemented in finite-precision arithmetic due to round-off errors. In this paper, we study the performance of recently introduced augmented complex least mean-square (ACLMS) algorithm when it is implemented in finite-precision arithmetic. To this aim, we first derive a model for the finite-precision ACLMS updating equations. Then, using the established energy conservation argument, we derive a closed-form expression, in terms of the excess mean-square error (EMSE) metric which explains how the quantized ACLMS (QACLMS) algorithm performs in the steady-state. We further derive the required conditions for mean stability of the QACLMS algorithm. The derived expression, supported by simulations, reveals that unlike the infinite-precision case, the EMSE curve for QACLMS is not monotonically increasing function of the step-size parameter. We use this observation to optimize the step-size learning parameter. Simulation results illustrate the theoretical findings. [ABSTRACT FROM AUTHOR]

In this paper, an efficient implementation of the Memory Improved Proportionate Affine Projection Algorithm (MIPAPA) is developed, for the adaptive identification of linear in parameters’ systems. The proposed algorithm offers an exact implementation of the MIPAPA scheme, while maintaining the computational burden at an affordable level. The performance of the proposed approach is illustrated by means of computer simulation, in the context of adaptive identification of sparse systems. Moreover, it is shown that the proposed algorithm can be applied for the efficient estimation of the communication channel in the transmission over optical fiber links using intensity modulated direct detection ON/OFF signaling. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to derive norm-constrained adaptive algorithms for sparse system identification. In contrast to other similar approaches found in the literature, the proposed approach is focused primarily on keeping the a posteriori error equal to zero (which is a characteristic of the normalized least-mean-square algorithm) while seeking to satisfy a norm constraint. To this end, the proposed algorithms look directly for a vector belonging to the intersection of a zero-error hyperplane and a hyperplane resulting from a relaxed norm constraint. This somewhat simpler strategy leads to effective sparsity-promoting adaptive algorithms that exhibit low computational complexity and use parameters that are easy to adjust. In this context, a general framework that allows obtaining adaptive algorithms using different norm functions is devised. From this framework, two norm-constrained algorithms based on the ℓ 1 and ℓ 0 norms are obtained. Moreover, enhanced versions of these algorithms are developed aiming to make them independent of user-defined norm-bound parameters. Numerical simulation results corroborate the effectiveness of the proposed framework as well as the very good performance of the obtained algorithms. [ABSTRACT FROM AUTHOR]

Lately, a number of image encryption algorithms that are either based on pixel level or bit level encryption have been proposed. However, not only pixel level permutation, but also bit level permutation has its intrinsic drawbacks. This paper proposes a new cryptosystem to address these drawbacks. Different kinds of permutation algorithms are first comprehensively analyzed and compared. Because, from a bit level perspective, an image can be considered as a natural three-dimensional (3D) bit matrix (width, height, and bit length), a new 3D bit matrix permutation is proposed, in which the Chen system is used to develop a random visiting mechanism to the bit level of the plain-image. By combining aspects of the Chen system with a 3D Cat map in the permutation stage, a new mapping rule is developed to map one random position to another random position (that is, double random position permutation) in the 3D matrix rather than using traditional sequential visiting to the plain-image. Simulations are carried out and the results confirm the security and efficiency of our new cryptosystem. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we design and analyze a Newton-like blind equalization algorithm for the APSK system. Specifically, we exploit the principle of minimum entropy deconvolution and derive a blind equalization cost function for APSK signals and optimize it using Newton׳s method. We study and evaluate the steady-state excess mean square error performance of the proposed algorithm using the concept of energy conservation. Numerical results depict a significant performance enhancement for the proposed scheme over well established blind equalization algorithms. Further, the analytical excess mean square error of the proposed algorithm is verified with computer simulations and is found to be in good conformation. [Copyright &y& Elsevier]

Abstract: This paper introduces a simple and effective chaotic system using a combination of two existing one-dimension (1D) chaotic maps (seed maps). Simulations and performance evaluations show that the proposed system is able to produce many 1D chaotic maps with larger chaotic ranges and better chaotic behaviors compared with their seed maps. To investigate its applications in multimedia security, a novel image encryption algorithm is proposed. Using a same set of security keys, this algorithm is able to generate a completely different encrypted image each time when it is applied to the same original image. Experiments and security analysis demonstrate the algorithm's excellent performance in image encryption and various attacks. [Copyright &y& Elsevier]

Abstract: The zero attracting normalized least mean square (ZA-NLMS) algorithm achieves lower steady-state error than the normalized least mean square (NLMS) algorithm for sparse system identification. Most of the available analytical results on several versions of the zero attracting least mean square algorithms assume white Gaussian inputs. This paper presents the individual weight error variance (IWV) analysis of the ZA-NLMS algorithm without Gaussian inputs assumption. The IWV analysis is based on exact individual weight error relation and used to derive the transient and steady-state behavior of the ZA-NLMS algorithm without restricting the input to being Gaussian or white, whereas some assumptions are introduced to overcome weight nonlinearity in evaluating certain expectations involved. Extensive simulations are used to verify the analysis results presented. [Copyright &y& Elsevier]

Abstract: Video transcoding is a legitimate operation widely used to modify video format in order to access the video content in the end-user's devices, which may have some limitations in the spatial and temporal resolutions, bit-rate and video coding standards. In many previous watermarking algorithms the embedded watermark is not able to survive video transcoding, because this operation is a combination of some aggressive attacks, especially when lower bit-rate coding is required in the target device. As a consequence of the transcoding operation, the embedded watermark may be lost. This paper proposes a robust video watermarking scheme against video transcoding performed on base-band domain. In order to obtain the watermark robustness against video transcoding, four criteria based on Human Visual System (HVS) are employed to embed a sufficiently robust watermark while preserving its imperceptibility. The quantization index modulation (QIM) algorithm is used to embed and detect the watermark in 2D-Discrete Cosine Transform (2D-DCT) domain. The watermark imperceptibility is evaluated by conventional peak signal to noise ratio (PSNR) and structural similarity index (SSIM), obtaining sufficiently good visual quality. Computer simulation results show the watermark robustness against video transcoding as well as common signal processing operations and intentional attacks for video sequences. [Copyright &y& Elsevier]

Abstract: According to the iterative identification technique and the hierarchical identification principle, this paper presents a two-stage gradient based and a least squares based iterative parameter estimation algorithms (i.e., the hierarchical gradient based iterative algorithm and the hierarchical least squares based iterative algorithm) for controlled autoregressive autoregressive moving average systems. The proposed two-stage least squares based iterative algorithm requires less computation compared with the least squares based iterative algorithm. The simulation results indicate that the two-stage least squares based iterative algorithm converges faster than the two-stage gradient based iterative algorithm. [Copyright &y& Elsevier]

Abstract: This paper introduces a new parametric switching chaotic system (PSCS) and its corresponding transforms for image encryption. The proposed PSCS has a simple structure and integrates the Logistic, Sine and Tent maps into one single system. The PSCS shows more general properties, including the Sine and Tent maps as special instances. It has complex chaotic behaviors. A novel image encryption algorithm is introduced using the proposed PSCS and its transforms. Simulation results and security analysis are given to demonstrate that the proposed algorithm can encrypt different types of images with a high level of security. [Copyright &y& Elsevier]

Abstract: In this paper, we introduce a subspace-augmented MUSIC technique for recovering the joint sparse support of a signal ensemble corrupted by additive impulsive noise. Our approach uses multiple vectors of random compressed measurements and employs fractional lower-order moments stemming from modeling the underlying signal statistics with symmetric alpha-stable distributions. We show through simulations that the recovery performance of the proposed method is particularly robust for a wide range of highly impulsive environments. Our subspace-augmented MUSIC achieves higher recovery rates than a recently introduced sparse Bayesian learning algorithm, which was shown to outperform many state-of-the-art techniques for joint sparse support recovery. [Copyright &y& Elsevier]

Abstract: In this paper, the maximum likelihood estimation (MLE) of the direction of departure (DOD) and direction of arrival (DOA) of multiple targets for bistatic multiple input multiple output (MIMO) radar is addressed. We derive the maximum likelihood estimator of the DOD and DOA with the assumption that the targets are unknown but deterministic. Moreover, we provide a compact expression of the Cramer Rao bound (CRB) under this nonrandom framework. Since the MLE of the target DOD and DOA is related to a high-dimensional nonlinear optimization problem, we propose alternating projection (AP) to solve it efficiently. Numerical simulations demonstrate that the AP based MLE can provide accurate estimations of the target DOD and DOA and achieves the CRB in the asymptotic region. Furthermore, results also show that the proposed algorithm outperforms the existing ESPRIT and MUSIC algorithm for the uniform linear array (ULA) configuration of the transmitted and received array. [Copyright &y& Elsevier]

Abstract: Parameter estimation methods have wide applications in signal processing, communication and system identification. This paper derives an iterative least squares algorithm to estimate the parameters of output error systems and uses the partitioned matrix inversion lemma to implement the proposed algorithm in order to enhance computational efficiencies. The simulation results show that the proposed algorithm works well. [Copyright &y& Elsevier]

Abstract: In this paper, an algorithm for sparse channel estimation, called least-absolutes (), and an algorithm for equalization, called linear least-absolutes (LLA), in non-Gaussian impulsive noise are proposed. The proposed approaches are based on the minimization of the absolute error function, rather than the squared error function. By replacing the standard modulus with the of complex numbers, the resulting optimization problem can be efficiently solved through linear programming. The selection of an appropriate regularization parameter is also addressed. Numerical results demonstrate that the proposed algorithms, compared with the classical methods, are more robust to impulsive noise and have a superior accuracy. [Copyright &y& Elsevier]

Abstract: The Fiedler vector of a graph is the eigenvector corresponding to the smallest non-trivial eigenvalue of the graph''s Laplacian matrix. The entries of the Fiedler vector are known to provide a powerful heuristic for topology inference, e.g., to identify densely connected node clusters, to search for bottleneck links in the information dissemination, or to increase the overall connectivity of the network. In this paper, we consider ad hoc networks where the nodes can process and exchange data in a synchronous fashion, and we propose a distributed algorithm for in-network estimation of the Fiedler vector and the algebraic connectivity of the corresponding network graph. The algorithm is fully scalable with respect to the network size in terms of per-node computational complexity and data transmission. Simulation results demonstrate the performance of the algorithm. [Copyright &y& Elsevier]

Abstract: A new sparse signal recovery algorithm for multiple-measurement vectors (MMV) problem is proposed in this paper. The sparse representation is iteratively drawn based on the idea of zero-point attracting projection (ZAP). In each iteration, the solution is first updated along the negative gradient direction of an approximate norm to encourage sparsity, and then projected to the solution space to satisfy the under-determined equation. A variable step size scheme is adopted further to accelerate the convergence as well as to improve the recovery accuracy. Numerical simulations demonstrate that the performance of the proposed algorithm exceeds the references in various aspects, as well as when applied to the modulated wideband converter, where recovering MMV problem is crucial to its performance. [Copyright &y& Elsevier]

Abstract: Available convergence analyses of adaptive active noise control systems apply to only theoretical cases with broad-band white noise or pure delay secondary paths. In order to investigate convergence behaviors of these systems in more practical conditions, this paper conducts a new convergence analysis for filtered-x LMS-based active noise control systems with band-limited white noise and moving average secondary paths. A linear model for the adaptation process is developed. Based on this model, the upper-bound of the adaptation step-size is derived. Also, the adaptation step-size leading to the fastest convergence rate is derived. In addition to the computer simulation, a fully implemented real time active noise control system is used to verify the validity of the analytical results. [Copyright &y& Elsevier]

Abstract: 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 function. In this paper, we perform convergence analysis of the ZA-LMS algorithm based on white input signals. The stability condition is examined and the steady-state mean square deviation (MSD) is derived in terms of the system sparsity, system response length, and filter parameters (step size and zero-attractor controller). In addition, we propose a criterion for parameter selection such that the ZA-LMS algorithm outperforms the standard LMS algorithm. The results are demonstrated through computer simulations. [Copyright &y& Elsevier]

Abstract: This article proposes an unbiased plain gradient algorithm for a second-order adaptive IIR notch filter with constrained poles and zeros. The proposed algorithm employs removing a dominant parameter that produces inherent bias. By using this technique, the performances are improved with slight expense in computational complexity. In this paper, theoretical analysis for deriving the estimations of bias and mean square error (MSE) at steady state are presented in closed form. Moreover, the stability bound of the algorithm is also derived. To confirm the analytical results, the computer simulations are provided to corroborate the effectiveness of the proposed algorithm. Furthermore, the performances of the algorithm are also compared with the plain gradient (PG) and modified plain gradient (MPG) algorithms. [Copyright &y& Elsevier]

Abstract: This paper proposes a new variable-step-size control for the normalized least-mean-square (NLMS) algorithm. A step-size vector is used, with a different value for each adaptive weight. With prior knowledge of the channel impulse response statistics, the optimal step-size vector is obtained by minimizing the mean-square deviation (MSD) between the optimal and estimated filter coefficients. In addition, filter convergence is proved and the relationship between the proposed and existing algorithms are analyzed. The proposed method achieves better steady-state performance compared to existing algorithms. The effectiveness of the proposed algorithm is demonstrated through computer simulations. [Copyright &y& Elsevier]

Abstract: In this paper a new method for direction of arrival estimation of direct sequence code division multiple access (DS-CDMA) signals in multipath fading environments is proposed. The method is based on suppressing the effect of coherent signals from different paths by orthogonal projection of the antenna''s spatial-temporal output vector onto the undesired subspace. The method assumes the knowledge of the spreading code sequence and delays of the paths of the desired user and then applies the MUSIC algorithm with new transformed vectors. This method is applicable to all array geometries and does not need any spatial smoothing that is usually applied for coherent signals. The estimates of the proposed method are consistent and its performance is investigated through simulations. [Copyright &y& Elsevier]

Abstract: The joint angle–frequency estimation (JAFE) based on the multidimensional ESPRIT is recently developed high-resolution parameter estimation technique for determining the directions of arrival (DOAs) and center frequencies of a number of narrow-band sources in a band of interest impinging on the far field of a planar array. In this paper, we present a simple and efficient joint angle–frequency estimation method via the unitary transformation, named as Unitary-JAFE algorithm. In the final stage of Unitary-JAFE, the matrix required to eigendecomposition is a complex unsymmetric matrix. For the eigendecomposition of a complex unsymmetric matrix, we will develop an effective algorithm based on Frame method and Newton iteration method, referred as Frame–Newton algorithm. The proposed method offers a number of advantages over other recently proposed ESPRIT-based techniques by taking advantage of the temporal smoothing, spatial smoothing, and forward–backward averaging techniques. Firstly, except for the final eigenvalue decomposition of dimension equal to the number of sources, it is efficiently formulated in terms of real-valued computation throughout. Secondly, in the final stage of the proposed algorithm, the real and imaginary parts of the i th eigenvalue of a matrix are one-to-one related to the frequency and direction of arrival (DOA) of the i th source. That is, the pairing of the estimated frequency and DOA is automatically determined. Thirdly, the proposed method avoids the joint diagonalization processing, which reduces the computation complexity. Finally, simulation results are presented verifying the efficacy of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: In actual nonlinear active noise control (NANC) systems, there often exist nonlinear distortions in such cases: the primary path may be nonlinear, the reference noise may exhibit nonlinear distortion, and the secondary path may have nonminimum-phase. To solve the problems of nonlinear distortions, two novel feedback adaptive filters based on the functional link neural network (FLNN) for NANC systems with low computational complexity are proposed in this paper, which are a feedback functional link neural network (FFLNN) and a reduced feedback functional link neural network (RFFLNN), respectively. To train the proposed nonlinear filters for NANC systems, a reduced complexity filtered-s least mean square (FSLMS) algorithm using filter bank approach is developed. The analysis of computational complexity shows that the RFFLNN adaptive filter involves less computation as compared to FFLNN and FLNN adaptive filters. Moreover, it is demonstrated through computer simulations for nonlinear noise processes that the RFFLNN adaptive filter outperforms FLNN and FFLNN in term of convergence speed and steady-state error. Furthermore, it is more effective in reducing nonlinear effects in NANC systems than other filters. [Copyright &y& Elsevier]

Abstract: In systems, such as hands-free mobile telephony, voice over internet protocol (VoIP), voice control, hearing aids, public address and teleconferencing, we are still faced with echo problem. Adaptive digital filters have been vastly used for the echo canceling by estimating the reverberation room impulse response. When a strength near-end talker (NET) signal exists, the adaptation process of the conventional algorithms misleads. In this paper, based on the correlation functions, we introduce a new algorithm named as adaptive step-size normalized correlation-based least mean square (ASNCLMS) algorithm, which is robust even in the presence of NET signal. This algorithm does not freeze the adaptation process during double-talk (DT) mode. Computer simulations, which prove the robustness of this algorithm, are also presented. [Copyright &y& Elsevier]

Abstract: Modified sign algorithm (MSA) for a second-order constrained adaptive IIR notch filter (ANF) is proposed and analyzed in this paper. Difference equations for the convergence in the mean and of the mean square, and steady-state estimation bias and MSE are derived in closed form. Moreover, a coarse stability for the MSA is carried out. Computer simulations are conducted to corroborate the theoretical analysis. [Copyright &y& Elsevier]

Abstract: Acoustic echo is an annoying issue for many hands-free telecommunication systems. Because of room acoustics and delay in the transmission path, echoes affect the sound quality and may hamper communications. Thus, acoustic echo cancellers (AECs) are critical for enhancing the audio quality. Echo cancellation is challenging because long room impulse response slows down the convergence rate and increases the computational complexity of the AEC designs. Nonlinearity of the power amplifier or the loudspeaker further exacerbates the problem. In this paper, we propose a novel AEC algorithm when the loudspeaker-enclosure-microphone system is described by a Hammerstein model. We show that by introducing a channel shortening filter, the length of the “effective” acoustic echo path is reduced considerably. Hence, the computational complexity of AECs is reduced and the convergence rate is increased. Adaptive algorithms are developed for the proposed structure and their convergence is shown analytically. The effectiveness of our method is illustrated by computer simulations. [Copyright &y& Elsevier]

Abstract: In this paper, a fast affine projection algorithm (FAPA), for the two-dimensional (2-D) adaptive linear filtering and prediction, is presented. The derivation of the proposed algorithm is based on the spatial shift-invariant properties of the 2-D discrete time signals. The proposed algorithm has low computational complexity, comparable to that of the 2-D LMS algorithm. The performance of the proposed scheme is comparable to that of the higher complexity 2-D RLS algorithms. The convergence speed and the tracking ability of the proposed 2-D FAPA algorithm are illustrated by computer simulation. [Copyright &y& Elsevier]

The linear least-squares algorithms for emitter localization such as the pseudolinear estimator are known to exhibit large estimation bias because of the correlation between the measurement matrix and the bearing noise. The paper presents a new bearings-only emitter localization algorithm with a closed-form solution that overcomes this bias by making use of instrumental variables obtained from a biased location estimate. By way of computer simulations, the new algorithm is shown to outperform the pseudolinear estimator while having a performance identical to that of the computationally more expensive maximum likelihood estimator. [Copyright &y& Elsevier]

This paper addresses the problem of interference suppression in direct sequence code division multiple access systems. We propose a novel semiblind decision feedback (DF) receiver based on the maximum likelihood principle that simultaneously exploits the transmission of training sequences and the statistical information of the unknown transmitted symbols. Both iterative and adaptive implementations of the proposed receiver, derived within the framework of the expectation maximization algorithm, are presented. Computer simulations show that the resulting multiuser detectors attain practically the same performance as the theoretical DF minimum mean square error receiver. [Copyright &y& Elsevier]

• We propose an improved ℓ 0 -LMS (ℓ 0 -ILMS) algorithm for sparse system identification. • The convergence condition of the proposed ℓ 0 -ILMS algorithm on step size is derived. • The parameter selection criterion for optimal steady-state mean-square deviation (MSD) is discussed. • Compared to ℓ 0 -LMS algorithm, the steady-state MSD of the proposed ℓ 0 -ILMS algorithm is lower and less sensitive to tuning parameters and measurement noise power. The ℓ 0 -norm-constraint LMS (ℓ 0 -LMS) algorithm is one of the widely used sparse LMS algorithms for the identification of sparse system, and its performance is quite attractive compared to other precursors. However, ℓ 0 -LMS is still confronted with some limitations on the optimal parameter selection and the estimated coefficient accuracy of sparse system identification. In this paper, we proposed an improved ℓ 0 -LMS (ℓ 0 -ILMS) algorithm to address these limitations. The convergence condition and the parameter selection rules for optimal steady-state mean-square deviation (MSD) of ℓ 0 -ILMS are discussed. Compared with ℓ 0 -LMS, the steady-state MSD of ℓ 0 -ILMS is lower and less sensitive to the tuning parameters and measurement noise power. Numerical simulations comparing the performance of standard LMS, ℓ 0 -LMS and ℓ 0 -ILMS demonstrate the effectiveness of ℓ 0 -ILMS. [ABSTRACT FROM AUTHOR]

• We derive the fused sparse diffusion algorithm to recover sparse parameter by enforcing sparsity of components themselves and similarity of adjacent components. • We provide the condition of convergence in the mean sense. • We study mean-square behavior and provide the expressions for calculating network steady-state MSD and steady-state individual weight error variance (IWEV). • Numerical simulations show that our proposed algorithm is able to achieve lower steady-state MSD than several other existing algorithms in both stationary and non-stationary environments. The problem of distributed estimation for sparse parameter has attracted much attention in recent years due to its wide applications. In some cases, the sparsity feature may also be found in the differences of successive components of the true parameter, i.e. the non-zero components may be assembled in one or more regions of the true parameter. In this paper, we propose the fused sparse diffusion LMS algorithm for recovering such parameter. The proposed algorithm relies on a sparse regularization term to enforce sparsity of components themselves, and fused sparse regularization term to enforce similarity of adjacent non-zero components. We then provide the condition of the proposed algorithm converging in the mean sense and study mean-square behaviors, which include mean square deviation (MSD) and individual weight error variance (IWEV). To further enhance estimation performance of the fused sparse diffusion algorithm, we provide an adaptive strategy in which the optimal regularization factors can be adjusted at each iteration. Finally, numerical simulations are conducted to show the superiority of our proposed algorithm over several other algorithms. [ABSTRACT FROM AUTHOR]