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This paper introduces a related model of membrane calculation in the defect detection and positioning of industrial components. It has the characteristics of distributed and parallel computing, and can efficiently search for better solutions in a given feature space. Inspired by the membrane clustering algorithm, this paper proposes a greedy membrane clustering algorithm and names it GMCA. GMCA is applied after the extraction of local features of normal samples. It uses a greedy strategy to construct a sub-feature set that describes the local characteristics of normal samples. During training, GMCA can learn the membrane cluster center of normal image blocks and each sub-feature within the cluster. At test time, the anomaly map is obtained by calculating the distance from the test sample block to the corresponding cluster center and the maximum distance from the cluster center to the nearest neighbor in the training sample. This solves the limitation of traditional algorithms requiring dataset alignment. In the unsupervised dataset MvTec AD, samples can be divided into object categories and texture categories according to the background of images. The pixel-level anomaly location index (AUROC) of this method on object category data reaches 98.3%. The image-level anomaly detection index (AUROC) on texture category data reaches 99.1%. • We design a computational model of membrane clustering using the evolutionary mechanisms and communicative mechanisms of cells. • GMCA has the global optimization characteristics of high accuracy and fast convergence of the membrane clustering algorithm. • GMCA has the local optimization characteristics of the greedy strategy. • GMCA solves the limitation of traditional defect detection and positioning methods that require dataset alignment. • Numerous experiments show the proposed GMCA performs competitively in industrial defect detection and location prediction. [ABSTRACT FROM AUTHOR]

A wealth of empirical evidence shows that people display opposite behaviors when deciding whether to rely on an algorithm, even if it is inexpensive to do so and using the algorithm should enhance their own performance. This paper develops a formal theory to explain some of these conflicting facts and submit new testable predictions. Drawing from decision analysis, I invoke two key notions: the 'value of information' and the 'value of control'. The value of information matters to users of algorithms like recommender systems and prediction machines, which essentially provide information. I find that ambiguity aversion or a subjective cost of employing an algorithm will tend to decrease the value of algorithmic information, while repeated exposure to an algorithm might not always increase this value. The value of control matters to users who may delegate decision making to an algorithm. I model how, under partial delegation, imperfect understanding of what the algorithm actually does (so the algorithm is in fact a black box) can cause algorithm aversion. Some possible remedies are formulated and discussed. • This paper initiates a formal decision-theoretic approach to make sense of the empirical evidence concerning people's attitudes towards algorithms. • This approach exploits two fundamental notions: the value of information and the value of control. • Ambiguity aversion will tend to decrease the value of algorithmic information; repeated exposure to algorithms may not increase it. • A first model of 'black box' algorithms is developed to analyze the value of keeping versus delegating control. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative bilevel optimization approach to elevate the deblurring process. By integrating a weights variable nonlocal model with a spatially varying attached term, the methodology aims to achieve enhanced restoration outcomes. Theoretical scrutiny is dedicated to unraveling the solution of the model, paving the way for the development of an efficient algorithm meticulously crafted to compute the clean image. This algorithm excels in learning both the weights parameter and the balanced L 2 - L 1 attached parameter concurrently, thereby ensuring optimal performance. Through careful parameter selection, the proposed nonlocal deblurring model showcases superior effectiveness, surpassing existing models in terms of both performance and efficacy. [ABSTRACT FROM AUTHOR]

• A fusion positioning scenario: closed corridor between buildings (With the development of urbanization, the positioning scene is gradually complicated.). • A novel fusion localization algorithm. (The adaptive factor function is established by using the median of residual, and the prior error covariance matrix is weighted to realize the balances the trust of the filter estimation value to the observation value and the state prediction value, then change the optimal estimation of the position.). • The proposed algorithm is compared with LS-KF, EKF, UKF and the improved LS-AKF algorithm proposed in reference [20] , and the results show that the proposed algorithm in this paper has a better effect. • Simulation experiment. To verify the stability of the fusion algorithm in the face of interference. According to the minimum detectable bias (MDB) obtained by internal reliability, different interference types and sizes are introduced, the proposed algorithm has stronger anti-interference ability. Due to the blocking and reflection of buildings, the received Beidou satellite navigation signal (BDS) in the closed corridor will be getting weaker, which results in poor positioning accuracy or positioning failure. Introducing Ultra-Wide Band (UWB) positioning technology and establishing BDS/UWB integrated positioning system is an effective method to achieve seamless positioning. The positioning accuracy obtained by the Least Square-Kalman Filter (LS-KF) algorithm is below m level. However, the prior error covariance matrix of the KF correction process is easily contaminated, which affects the proportion of system observations and state prediction values to the optimal position estimation. A LS-KF fusion positioning algorithm based on adaptive error covariance matrix is proposed, the algorithm adjusts the state prior error covariance matrix by constructing an adaptive factor, improves the Kalman gain and the optimal estimation of the position, balances the trust of the filter estimation value to the observation value and the state prediction value of the integrated positioning system. Theoretical analysis and experimental results show that compared with LS-KF, EKF, UKF and improved LS-KF algorithm, the proposed LS-AKF algorithm not only has higher positioning accuracy, stronger anti-interference ability, but also has faster convergence speed. [ABSTRACT FROM AUTHOR]

In conventional sparse Space-Time Adaptive Processing (STAP) algorithms, the l 0 -norm is often used instead of the l 1 -norm to relax convexity constraints. The approach presented in this paper can be mathematically reformulated as a linear problem, which can be efficiently solved using various convex optimization techniques. While this method effectively circumvents the NP-Hard complexity associated with the l 0 -norm, it does come with certain potential challenges. For example, the observation matrix must meet the criteria of the Restricted Isometry Property (RIP) and other stringent conditions. In this research, a novel STAP algorithm is introduced, which is based on a non-convex penalty function. This innovative algorithm substitutes the traditional l 0 -norm with a custom-designed non-convex penalty function and is applied to the Direct Filter Processor (DFP), solved using the recursive least squares (RLS) algorithm. The resulting algorithm, known as g p -RLS, is demonstrated through Monte Carlo simulations to outperform other l 1 -based and reduced-rank STAP algorithms in terms of faster convergence, improved signal-to-clutter-plus-noise ratio (SCNR), and enhanced target detection performance. [ABSTRACT FROM AUTHOR]

As a blind equalization algorithm, the fractional lower-order statistics based multimodulus algorithm (FLOS-MMA) is suitable for equalizing multilevel quadrature amplitude modulation (QAM) signals in impulsive noise. However, the equalization performance of FLOS-MMA is severely degraded in strong impulsive noise because the first-order gradient of the cost function constrains the ability of FLOS to suppress large outliers in the equalizer output signal and is sensitive to large outliers in the equalizer input signal. To enhance the robustness of multimodulus blind equalization algorithm against impulsive noise, a FLOS based momentum fractional-order multimodulus algorithm (FLOS-MFOMMA) is proposed in this paper. The proposed algorithm mitigates the adverse effects of impulsive noise on the updating of equalizer coefficients by utilizing the fractional-order gradient and accelerates the convergence speed by incorporating a momentum term. Then, the steady-state excess mean square error and the computational complexity of FLOS-MFOMMA are analyzed theoretically. Finally, numerical simulations, for equalizing 16-QAM signals under impulsive noise environments, show that the proposed algorithm is superior to FLOS-MMA in terms of robustness and convergence speed. [ABSTRACT FROM AUTHOR]

Many action recognition methods require significant computational resources to achieve good results on unedited videos. However, their performance on infrared videos, which contain less information, is often unsatisfactory. In this paper, we propose a multi-scale difference joint key frame extraction algorithm for action recognition in infrared surveillance videos. To evaluate our algorithm, we have created a self-built dataset comprising 1200 unedited infrared surveillance videos categorized into 10 action categories. Our algorithm extracts key frames by jointly analyzing the global and local differences between adjacent frames. Experimental analysis demonstrates that by using only 10 frames, our algorithm improves the accuracy of generic action recognition algorithms by more than 10% on both our self-built dataset and the Infrared-Visible dataset. Moreover, our proposed method achieves high recognition accuracy with minimal computational cost, even when using a small number of frames. It outperforms state-of-the-art methods by 1.82% on the Infrared-Visible dataset and 1.35% on the InfAR dataset. These results highlight the effectiveness of our algorithm as a preprocessing module to significantly enhance the accuracy of action recognition algorithms prior to employing generic models. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of identifying ship motion parameters and wave peak frequency. Utilising the Euler discretisation principle, we establish a discrete-time auto-regressive moving average model with exogenous input (ARMAX) for the ship-wave system. Furthermore, we develop a filtering-based stochastic gradient algorithm for the system by applying filtering techniques and auxiliary model identification idea. A filtering-based multi-innovation stochastic gradient algorithm, utilizing the multi-innovation identification theory, was developed to enhance the convergence rate and accuracy of parameter identification. This approach was found to be more effective than the filtering-based stochastic gradient algorithm. Simulation results validate the efficacy of the proposed algorithm in parameter identification. • Based on the Eulerian discretization idea, a ship-wave discrete-time autoregressive moving average model with exogenous inputs is derived. • Introducing the filtering technique and the auxiliary model identification idea, a filtered stochastic gradient algorithm is proposed. • A filtering-based multi-innovation stochastic gradient algorithm is proposed based on filtered stochastic gradient identification. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative methodology to establish consensus among distributed information processing nodes (DIPNs) in the context of multi-target tracking (MTT) within environments characterized by resource constraints and communication uncertainties. Through the integration of the event-triggered (ET) strategy with a consensus-based algorithm, existing approaches foster consensus among DIPNs while simultaneously conserving communication resources. Nonetheless, a systematic investigation into the comprehensive analysis of data reliability from each node has not been conducted. Combining anomalous data resulting from communication uncertainties with other data on an equal footing leads to inaccurate results. To address this issue, we apply the multiple-model algorithm, assigning consensus weight to each DIPN based on the motion model distribution of the same target observed by different DIPN. Additionally, we introduce an auxiliary ET marker, considering the divergence in the motion model distribution between two consecutive moments of a certain target. This marker assists in determining whether local information must be transmitted to other DIPNs. The proposed approach yields more accurate and congruent output results from each DIPN in comparison to conventional methods, given the same triggering frequency. Numerical simulations demonstrate the efficacy of the suggested approaches in a distributed MTT scenario. • We apply the multiple-model generalized labeled multi-Bernoulli filter. • We combine event-triggered strategy with consensus approach to reduce communication. • We check data reliability from each node to improve multi-target tracking accuracy. • We suggest an additional event-triggered mark to locate the message for transmitting. [ABSTRACT FROM AUTHOR]

The presence of noise is a pervasive issue that significantly impacts the performance of adaptive filtering algorithms. To address this challenge, the bias compensation technique has recently emerged, involving the incorporation of an additional term into the update equation. Despite the apparent simplicity of the bias-compensated least-mean-squares algorithm, conducting a comprehensive theoretical analysis of its performance is a complex task. In this paper, we undertake an exact expectation analysis to demonstrate the asymptotic unbiasedness of the algorithm, even when disregarding the commonly assumed theory of independence between adaptive coefficients and input data. Furthermore, to improve the understanding of the algorithm's stability, we employ a stochastic model that assumes independence between the radial and angular distributions of the input vector. The resulting model is intricate, necessitating heuristic approximations to derive practical insights. Notably, our analysis reveals that the upper bound on the step size of the bias-compensated least-mean-squares algorithm consistently remains smaller than that of the least-mean-squares algorithm. These findings gain robust support from extensive simulations. [ABSTRACT FROM AUTHOR]

In this paper, we design a low-complexity direction-of-arrival (DOA) estimation algorithm based on the unitary approximate message passing (UAMP) and Bernoulli-Gaussian (BG) prior. We first show that the estimation of DOA can be transferred into a sparse signal recovery problem, where we turn to UAMP with damping technique to solve this problem. Furthermore, we assume the BG prior on the sparse vector to be estimated, resulting in the fast estimation of the positions of non-zero elements. Moreover, expectation maximum (EM) is leveraged to automatically learn the BG parameters. Compared to the state-of-the-art UAMP-based algorithm with sparse Bayesian learning (SBL), the proposed approach can achieve the same DOA mean square error (MSE) performance with a much faster convergence speed. [ABSTRACT FROM AUTHOR]

Nonlinear adaptive filters, such as Volterra and functional link adaptive filter (FLAF), face increased computational complexity with rising input dimensionality or expansion order. To enhance computational efficiency, this paper introduces a nonlinear adaptive algorithm based on Rectified Linear Unit (ReLU). Applying ReLU to a tonal signal generates components corresponding to the original frequency and even harmonics. Additionally, applying ReLU to the sum of two tonal signals yields a spectrum encompassing intermodulation distortion frequencies. These traits enable the algorithm to mitigate harmonic and intermodulation distortions while maintaining judicious computational complexity. The numerical simulation results for the identification of nonlinear systems validate the algorithm's ability to fit harmonic and intermodulation nonlinear distortions. In simulations of nonlinear echo cancellation with diverse test signals and various distortion models, our proposed algorithm consistently demonstrates competitive performance, notably excelling in music and speech scenarios. [ABSTRACT FROM AUTHOR]

The Elo algorithm, renowned for its simplicity, is widely used for rating in sports tournaments and other applications. However, despite its widespread use, a detailed understanding of the convergence characteristics of the Elo algorithm is still lacking. Aiming to fill this gap, this paper presents a comprehensive (stochastic) analysis of the Elo algorithm, considering round-robin tournaments. Specifically, analytical expressions are derived describing the evolution of the skills and performance metrics. Then, taking into account the relationship between the behavior of the algorithm and the step-size value, which is a hyperparameter that can be controlled, design guidelines and discussions about the performance of the algorithm are provided. Experimental results are shown confirming the accuracy of the analysis and illustrating the applicability of the theoretical findings using real-world data obtained from SuperLega, the Italian volleyball league. [ABSTRACT FROM AUTHOR]

The existing methodologies for hybrid narrow-band active noise control (HNANC) systems utilises variants of the FXLMS algorithm, which typically resulted in degraded convergence and steady-state performance. In this paper, a class of variable step size (VSS) adaptive algorithms are explicitly developed for a HNANC system analyses and the convergence as well as the steady-state performance of the proposed framework are evaluated in comparison to the existing state-of-the-art. For an HNANC system designed to operate in a multiple noise environment, the VSS filtered-x LMS (VSSFXLMS) and momentum VSS filtered- x weight accumulated least mean square (MVSS-FXWALMS) algorithms are proposed. The proposed algorithm employs a variable step size that is inspired by the variable momentum factor, and combines these two parameters to drive the FXWALMS algorithm for an HNANC system in order to accomplish maximum noise reduction. In a multiple noise environment setup for an HNANC system, the proposed algorithm surpasses the state-of-the-art in nearly all conventional operating conditions. A detailed performance analysis of the proposed algorithm has been carried out using mean noise reduction ratio, steady-state power spectra at different conditions. The results demonstrate a significant improvement in noise reduction performance compared to the counterparts. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) imaging is generally characterized by a large amount of data and a high sampling rate. The traditional one-bit compressed sensing will generate virtual targets when recovering images at a low SNR, resulting in low reconstruction accuracy and obvious noise impact of the algorithm. In this paper, a one-bit SAR imaging algorithm based on the Minimax Concave (MC) penalty function and the Total Variation (TV) norm is proposed, which can potentially improve the reconstruction accuracy. At the same time, to solve the problem of speckle noise generated in the imaging process, this paper proposes a Fast Low-rank Sparse Decomposition (FLRSD) algorithm based on the one-bit contaminated image, which potentially improves the anti-noise performance and reconstruction efficiency. The results of simulation and measured data show that the proposed algorithms have better reconstruction accuracy and focusing performance than other algorithms. Even at low SNR, they also have good reconstruction effect. [ABSTRACT FROM AUTHOR]

The iterative shrinkage-thresholding algorithm (ISTA) is often used to address the convolutional dictionary learning problem. However, the ISTA algorithm is easy to fall into the local minimum, and some atoms in the convolution dictionary change little when updated. Therefore, in this paper, we propose an improved ISTA algorithm, iterative shrinkage-thresholding algorithm with inertia and dry friction (ISTA-IDF). An inertia is introduced to avoid falling into the local minimum but generating an oscillatory behavior since the values of the objective function is not monotonic decreasing, which can be alleviated by dry friction. From the perspective of dynamics, ISTA can be seen as an iteration algorithm deduced by a forward-backward Euler discretization of a first-order dynamic system, and ISTA-IDF can be regarded as the forward-backward Euler discretization of a second-order heavy-ball system with dry friction (HBDF). So using the dynamics theory, we establish the linear and finite convergence property of ISTA-IDF. The experimental results on image reconstruction and denoising tasks demonstrate the superior performance of the proposed ISTA-IDF compared with ISTA and FISTA. • This paper proposes an improved ISTA algorithm with the inertia and dry friction damping terms for the CDL problem. • The linear and finite convergence property of ISTA-IDF is established. • We conducted experiments on image reconstruction and denoising to demonstrate the effectiveness of ISTA-IDF. [ABSTRACT FROM AUTHOR]

Highlights • In this paper, we focus on the recursive least-squares algorithm for bilinear forms, i.e., RLS-BF. • The bilinear term is defined with respect to the impulse responses of a spatiotemporal model. • Low-complexity versions of the RLS-BF algorithm are proposed, based on the DCD method. • A robust variable-regularized RLS-BF (VR-RLS-BF) algorithm is developed, using an estimate of the SNR. • Low-complexity versions of the VR-RLS-BF algorithm are derived, involving the DCD method. Abstract Due to its fast convergence rate, the recursive least-squares (RLS) algorithm is very popular in many applications of adaptive filtering, including system identification scenarios. However, the computational complexity of this algorithm represents a major limitation in applications that involve long filters. Moreover, when the parameter space becomes large, the system identification problem is more challenging and the adaptive filters should be able to cope with this aspect. In this paper, we focus on the identification of bilinear forms, where the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. From this perspective, the solution requires a multidimensional adaptive filtering technique. Recently, the RLS algorithm tailored for bilinear forms (namely RLS-BF) was developed for this purpose. In this framework, the contribution of this paper is mainly twofold. First, in order to reduce the computational complexity of the RLS-BF algorithm, two versions based on the dichotomous coordinate descent (DCD) method are proposed; due to its arithmetic features, the DCD algorithm represents one of the most attractive alternatives to solve the normal equations. However, in the bilinear context, we need to consider the particular structure of the input data and the additional related challenges. Second, in order to improve the robustness of the RLS-BF algorithm in noisy environments, a regularized version is developed, together with a method to find the regularization parameters, which are related to the signal-to-noise ratio (SNR). Furthermore, using a proper estimation of the SNR, a variable-regularized RLS-BF algorithm is designed and two DCD-based low-complexity versions are proposed. Due to their nature, these variable-regularized algorithms have good robustness features against additive noise, which make them behave well in different noisy condition scenarios. Simulation results indicate the good performance of the proposed low-complexity RLS-BF algorithms, with appealing features for practical implementations. [ABSTRACT FROM AUTHOR]

Abstract In this paper, an optimized complex nonnegative tensor factor 2D deconvolution (CNTF2D) is proposed to separate the sources that have been mixed in an underdetermined reverberant environment. Unlike conventional methods, the proposed model decomposition is performed directly on the statistics in the form of spectral covariance matrix instead of the data itself (i.e. the mixed signal). For faster convergence the model is adapted under the hybrid framework of the generalized expectation maximization and multiplicative update algorithms. This paper also proposes a solution to the issue of optimizing the model order i.e., number of components and convolutive parameters in the CNTF2D model. To this end, a latent-observation model based on Gamma-Exponential process is developed. In addition, the proposed Gamma-Exponential process can be used to initialize the parameterization of the CNTF2D. The proposed algorithm encodes a set of variable sparsity parameters derived from the Gibbs distribution. This permits a stable update and optimizes the CNTF2D with the correct degree of sparseness in the time-frequency domain. Experimental results on the underdetermined reverberant mixing environment have shown that the proposed algorithm is effective at separating the mixture with an average signal-to-distortion ratio of 2.5 dB. Highlights • Proposal of a complex tensor deconvolution model with full-rank spatial covariance matrix. • Tensor factorization works directly on the statistics (spectral covariance matrix) instead of the mixed signal. • Optimizing the sparsity level of each individual temporal code via the Gibbs distribution. • Optimizing the tensor model order based on Gamma-Exponential observation process. [ABSTRACT FROM AUTHOR]

In practical FIR digital filter applications it is often necessary to represent the filter coefficients with a finite number of bits. The finite wordlength coefficient restriction reduces the filter quality and increases the filter deviation. This increase can be reduced substantially if the optimal finite wordlength coefficients are used. Computing these coefficients is more difficult and can be slow. It was shown before that the computation time can be significantly reduced with the help of a lower bound on the deviation increase. Derivation of a new algorithm that uses a much better lower bound is the purpose of this paper. We consider the general case of a length N filter with a discrete set of allowable real coefficients and demonstrate the effectiveness of the new algorithm on a set of filter design cases. The results show that this algorithm reduces the computation times up to 3 times when compared with the previously best results. • Practical FIR filter implementations often require low bit filter coefficients. • Quantization of FIR filter coefficients reduces the filter quality. • Optimal finite wordlength FIR filter coefficients are much better. • Computing the optimal low bit coefficients can be computationally demanding. • A new faster algorithm for optimal finite wordlength FIR design is a solution. [ABSTRACT FROM AUTHOR]

Aiming the extract micro-motion features of rotor targets when the flashes are in the echo, this paper proposes a method based on the GS-IRadon algorithm. At first, we analyze the time-frequency characteristics of rotor targets through a scattering point signal model. Then, we analyze and study the usage conditions of the algorithm and use the Moving Target Indication (MTI) algorithm to remove the direct-current (DC) frequency component in signal's time-frequency results. Finally, we utilize the Inverse Radon transform algorithm based on the golden section method (GS-IRadon) to extract the target micro-motion features. The estimation of speed, length, and number of blades in the presence of flashes phenomenon in rotor targets was achieved. Simulation results showed that the algorithm can significantly reduce computational complexity while maintaining high estimation accuracy and has a more stable performance than regular algorithms. [ABSTRACT FROM AUTHOR]

Clock synchronization is crucial in time-sensitive networks (TSN) to ensure the normal operation of devices. However, traditional synchronization techniques face challenges such as the accumulation of synchronization errors over multiple hops and slow synchronization speed, which hinder the development of large-scale and multi-hop TSN. In this paper, we propose a cooperative synchronization scheme based on two-way timestamp exchange to reduce error accumulation and enable synchronization information exchange between any node in the network and the reference node. Moreover, we develop a fast Kalman filter (FKF) algorithm to estimate clock offset and clock skew. To reduce time complexity, we replace the calculation of gain matrices with extremely high dimension with a small matrix based on the randomization matrix approximation in the updating phase of the Kalman Filter. Simulation results show that the proposed method significantly reduces synchronization complexity without sacrificing clock synchronization accuracy, making it highly promising for future TSN deployments. [ABSTRACT FROM AUTHOR]

Distributed array radar is a next generation radar that has been a hot research direction due to its high maneuverability, low cost, and high reliability. However, the synthesized beam pattern of the distributed array usually has some high sidelobes and grating lobes, which reduce the system performance. In this paper, a hybrid algorithm based on the differential evolution algorithm and modified iterative Fourier transform algorithm called DE-MIFT is proposed for optimizing subarray positions in a distributed array model. This distributed array model is based on a uniform linear subarray with equal amplitude excitation. Moreover, an integer coding method based on the minimum spacing between subarrays is adopted in the proposed algorithm to encode and initialize population, then the two algorithms are used to optimize in parallel. Then a variable selection factor is used to select dominant individuals from the parallel optimization results, and the subarray position design of distributed array is obtained through iterations. It is shown by simulation results that the proposed algorithm can effectively suppress system grating lobes, reduce sidelobe levels, and has powerful global search capability and fast convergence speed. [ABSTRACT FROM AUTHOR]

This paper concentrates on the location methods for strictly noncircular sources by widely separated arrays. The conventional two-step methods extract measurement parameters and then, estimate the positions from them. Compared with the conventional two-step methods, direct position determination (DPD) is a promising technique, which locates transmitters directly from original sensor outputs without estimating intermediate parameters in a single step, and thus, improves the location accuracy and avoids the data association problem. However, existing DPD methods mainly focus on complex circular sources without considering noncircular signals, which can be exploited to enhance the localization accuracy. This paper proposes a maximum likelihood (ML)-based DPD algorithm for strictly noncircular sources whose waveforms are unknown. By exploiting the noncircularity of sources, we establish an ML-based function in time domain under the constraint on the waveforms of signals. A decoupled iterative method is developed to solve the prescribed ML estimator with a moderate complexity. In addition, we derive the deterministic Cramér–Rao Bound (CRB) for strictly noncircular sources, and prove that this CRB is upper bounded by the associated CRB for circular signals. Simulation results demonstrate that the proposed algorithm has a fast convergence rate, and outperforms the other location methods in a wide range of scenarios. [ABSTRACT FROM AUTHOR]

To locate multiple sources through time-difference-of-arrival (TDOA) measurements, existing algorithms generally require the matching relationship between measurements and the corresponding sources. In this paper, we propose a new Bayesian learning method for cases where the matching relationship is not given and off-grid error is considered. To achieve this, first we propose a new basis generator, which casts the localization problem within the Bayesian learning scheme. Then, we modify the existing sparse Bayesian inference (SBI) approaches and explore the priors on fingerprinting weights, resulting in two intermediate algorithms. On these foundations, a subspace-based robust SBI (SRSBI) algorithm is proposed as the core of this paper. SRSBI is highlighted by its ability to work free from iteration when estimating off-grid targets. What' more, SRSBI offers considerable robustness against initial guesses of hyper-parameters. Numerical simulations demonstrate the superiority of SRSBI in terms of accuracy, robustness and speed, compared to the other reported ones. [ABSTRACT FROM AUTHOR]

The paper discusses the separation of partially overlapping data packets by an antenna array in narrowband communication systems. This problem occurs in asynchronous communication systems and several transponder systems such as Radio Frequency Identification (RFID) for wireless tags, Automatic Identification System (AIS) for ships, and Secondary Surveillance Radar (SSR) and Automatic Dependent Surveillance—Broadcast (ADS—B) for aircraft. Partially overlapping data packages also occur as inter-cell interference in mutually unsynchronized communication systems. Arbitrary arrival times of the overlapping packets cause nonstationary scenarios and makes it difficult to identify the signals using standard blind beamforming techniques. After selecting an observation interval, we propose subspace-based algorithms to suppress partially present (interfering) packets, as a preprocessing step for existing blind beamforming algorithms that assume stationary (fully overlapping) sources. The proposed algorithms are based on a subspace intersection, computed using a generalized singular value decomposition (GSVD) or a generalized eigenvalue decomposition (GEVD). In the second part of the paper, the algorithm is refined using a recently developed subspace estimation tool, the Signed URV algorithm, which is closely related to the GSVD but can be computed non-iteratively and allows for efficient subspace tracking. Simulation results show that the proposed algorithms significantly improve the performance of classical algorithms designed for block stationary scenarios in cases where asynchronous co-channel interference is present. An example on experimental data from the AIS ship transponder system confirms the effectiveness of the proposed algorithms in a real application. [ABSTRACT FROM AUTHOR]

Receiver detection algorithm has always been a research hotspot in communication system and the key point to determine the performance of 5G system. Both industry and academia have been committed to finding detection algorithms with lower complexity and higher detection performance. This paper will study how to solve the problems of low detection performance and high complexity of classical algorithms. Taking Pattern Division Multiple Access (PDMA) as an example, this paper improves the existing receiver detection algorithms such as Minimum Mean Square Error-Serial Interference Cancellation (MMSE-SIC), Message Passing Algorithm (MPA) and Expectation Propagation Algorithm (EPA), and puts forward corresponding improved algorithms with specific formulas, i.e. Enhanced-MMSE-SIC, Turbo-like MPA (MPA-Turbo) and Turbo-like EPA with hybrid parallel interference cancellation (EPA-Turbo-PIC). Link-level simulation results and analysis show that the proposed improved algorithms have performance advantages over the corresponding traditional algorithms. [ABSTRACT FROM AUTHOR]

The direction-of-arrival (DOA) estimation problem with a few noisy snapshots can be formulated as a problem of finding a joint sparse representation of multiple measurement vectors (MMV), and some algorithms based on compressive sensing (CS), such as the joint ℓ 0 approximation DOA (JLZA-DOA) and Multiple Snapshot Matching Pursuit Direction of Arrival (MSMPDOA) algorithms, have recently been proposed for solving this problem. Compared with the conventional DOA methods, the CS-based methods can achieve super-resolution by using only small number of snapshots, without the necessity of an accurate initialization, with small sensitivity to the correlation of the source signals. However, these CS-based algorithms usually do not work well in low signal-noise ratio (SNR) environment. In addition, the increased number of sensors in massive multiple-input-multiple-output (MIMO) systems lead to a huge matrix, and the matrix inversion operation in each iteration of the CS-based algorithm results in a relatively high computational cost. The purpose of this paper is to propose a novel adaptive filtering algorithm, i.e., the ℓ 2 , 0 -least mean square (ℓ 2 , 0 -LMS) algorithm, which can be viewed as a generalization of the ℓ 0 -LMS algorithm for single measurement vector (SMV) problem. Our proposed algorithm incorporates a mixed norm (ℓ 2 , 0 -norm) to treat the joint sparsity and inherits the robustness against noise and the low complexity of the ℓ 0 -LMS algorithm, and can thus work well for massive MIMO systems. Numerical experiments demonstrate that the proposed algorithm can achieve much better estimation performance with a lower computational cost than the existing ones. [ABSTRACT FROM AUTHOR]

In this paper, multi-group multicast beamforming is considered for the full digital and hybrid beamforming. The wireless system comprises of a multiple-antenna base station and single-antenna users. Quality-of-service (QoS)-aware design is investigated where the optimization objective is to minimize the total transmitted power subject to the signal-to-interference-plus-noise ratio (SINR) constraint at each user. In addition to the SINR constraints, per-antenna power constraint is included for each antenna of the base station. The original optimization problem for full digital beamforming is transformed into an equivalent form such that the alternating direction method of multipliers (ADMM) can be applied in an effective and computationally inexpensive manner for the large-scale antenna systems. In this new formulation, the beamformer weight vectors are decomposed into two subspaces in order to decrease the number of dual variables and multiplications. The optimum update equations are obtained for the proposed ADMM algorithm. This new reformulation is used for two different hybrid beamforming structures employing phase shifters and vector modulators. Optimum updates are derived for each system. The proposed algorithms decrease computational complexity of the existing ADMM algorithms due to the effective reformulation as well as the direct solution of the nonconvex problem. In the simulation results, it is shown that the proposed methods have better convergence behavior and less computational time than the benchmark algorithms. Furthermore, the proposed method for hybrid beamforming with vector modulators performs better than its counterpart in the literature in terms of transmitted power. [ABSTRACT FROM AUTHOR]

In this paper, we propose, describe, and test a modification of the K-SVD algorithm. Given a set of training data, the proposed algorithm computes an overcomplete dictionary by minimizing the β -divergence (β > = 1) between the data and its representation as linear combinations of atoms of the dictionary, under strict sparsity restrictions. For the special case β = 2 , the proposed algorithm minimizes the Frobenius norm and, therefore, for β = 2 the proposed algorithm is equivalent to the original K-SVD algorithm. We describe the modifications needed and discuss the possible shortcomings of the new algorithm. The algorithm is tested with random matrices and with an example based on speech separation. [ABSTRACT FROM AUTHOR]

A stochastic analysis of the probabilistic least-mean-square (Prob-LMS) algorithm would be a useful guideline for designing the adaptive filter. However, no analytical expressions for the stochastic analysis of the Prob-LMS algorithm have been reported in the literature. Hence, this paper analyzes the mean-deviation and mean-square-deviation (MSD) behavior of the Prob-LMS algorithm for the general case of an unknown Gauss-Markov channel. Analytical expressions are derived for the transient and steady-state MSD of the Prob-LMS algorithm. Monte Carlo simulations for fixed and time varying channels show excellent agreement between the simulated and theoretical MSD for a wide range of parameters such as SNR, filter length and input signal statistics. Monte Carlo MSD simulation results are presented for the Prob-LMS algorithm which compare favorably to several well-known VSS algorithms. [ABSTRACT FROM AUTHOR]

Non-stationary jamming signals broadcast by jammers severely threaten the reliability of global navigation satellite systems (GNSS). The instantaneous frequency (IF) based subspace projection algorithm is an attractive solution for GNSS jamming mitigation. However, its theoretical performance analysis is rare and not well discussed. To better apply subspace projection, we develop the theoretical post-correlation signal-to-interference-plus-noise power ratio (SINR) of receivers incorporating subspace projection to facilitate performance analysis. Different from existing work, this paper assumes that IF errors are independent and identically distributed instead of instantaneous phase errors, which is more reasonable. Numerical simulation results validate the derived theoretical SINR. The results show that the performance of subspace projection highly depends on the selection of projection length. Subspace projection with a well-designed projection length can perform much better than a conventional long projection length. Utilizing the theoretical SINR, the jamming mitigation performance of subspace projection with different projection lengths can be quickly evaluated, which provides an important theoretical guidance for the application of the algorithm. • Theoretical performance of subspace projection algorithm is developed based on the assumption that IF errors are IID. • The performance of subspace projection for GNSS jamming mitigation highly depends on the selection of projection length. • The theoretical performance provides an important guidance for a better application of subspace projection. [ABSTRACT FROM AUTHOR]

In this paper, we study the regularization l p -norm minimization problem to recover the sparse signals. We first prove that every global optimal solution to the regularization l p -norm minimization problem also solves the l 0 -norm minimization problem if the certain conditions are satisfied, and then generate a truncated approximated difference algorithm to recover the sparse signals. At last, we provide some numerical simulations to test the performance of the truncated approximated difference algorithm, and the numerical results show that the proposed algorithm performs effectively in recovering the sparse signals compared with some state-of-art methods. [ABSTRACT FROM AUTHOR]

In this paper we propose a new training algorithm for a class of polynomial models. The algorithm is derived using a learning bound for predictors that are convex combinations of functions from simpler classes. In our case, the hypotheses are polynomials over the input features, and they are interpreted as convex combinations of homogeneous polynomials. In addition, the coefficients are restricted to be positive and to sum to 1. This constraint will simplify the interpretation of the model. The training is done by minimizing a surrogate of the learning bound, using an iterative two-phase algorithm. Basically, in the first phase the algorithm decides which monomials of higher degree should be added, and in the second phase the coefficients are recomputed by solving a convex program. We performed several experiments on binary classification datasets from different domains. Experiments show that the algorithm compares favorably in terms of accuracy and speed with other classification methods, including some new interpretable methods like Neural Additive Models and CORELS. In addition, the resulting predictor can sometimes be understood and validated by a domain expert. The code is publicly available. [ABSTRACT FROM AUTHOR]

This paper proposes a new color image encryption algorithm based on modulo negabinary code (MNBC-IEA), which treats a color pixel as one 24-bit unsigned integer rather than three 8-bit unsigned integers, and it makes the 24-bit-planes dependent on each other. We first provide a new coding scheme of unsigned integers called modulo negabinary code (MNBC), and we construct its encoding and decoding algorithm. Next, we use MNBC to design the substitution algorithm for color image encryption. MNBC-IEA consists of the enhanced MNBC, a random cyclic shift (RCS), and a diffusion procedure. The RCS procedure is composed of the RCSs in three directions, and it treats a color image as a cuboid. Hence, the permutation process can cause the 24-bit-planes to affect each other in addition to scrambling the bit positions. The enhanced MNBC can change the grayscale values via a process that encodes a 28-bit unsigned integer consisting of a 24-bit pixel value and a 4-bit key into the MNBC. The diffusion procedure includes exclusive OR and modulo arithmetic such that plaintext pixels, ciphertext pixels, and keystreams are related to each other. The final experiments indicate that our algorithm can encrypt a color image into a random binary sequence and resist various statistical, differential, and brute-force attacks. [ABSTRACT FROM AUTHOR]

In underwater acoustics, compressive sensing was applied to the separation of multiple raypaths based on direction of arrival (DOA) estimation. While it is impossible to separate raypaths directly without eliminating the interference between raypaths. This paper proposes a subspace-based higher-order compressive sensing algorithm, which enables separating raypaths interrupted by colored noise. In the algorithm, a trispectrum matrix is first calculated from the signal received on the reception array. To improve the robustness against noise, the trispectrum matrix is decomposed into signal and noise subspaces using an eigenvalue decomposition. Then, a subspace-based higher-order convex optimization problem is designed based on the signal subspace. The separation of raypaths is cast as the optimization problem solved in a greedy strategy approximation. The results obtained in both simulations and experiments show that the present method not only achieves the more precise separation of raypaths, but it also provides better suppression of noise as compared to the other algorithms. [ABSTRACT FROM AUTHOR]

To improve the performance of reversible data hiding on JPEG images, a multi-level sorting prediction enhancement-based two-dimensional reversible data hiding algorithm for JPEG images is proposed in this paper. Firstly, the smoothness of each DCT block is evaluated by counting the location of the last non-zero coefficient in each DCT block. Then, the gradient of the object block is calculated with the DC coefficients of its surrounding blocks, and the lowest gradient direction blocks are employed to achieve the prediction of object block AC coefficients. Secondly, these gradient of adjacent blocks of the object block are compared to found the lowest gradient direction. The AC coefficient of the object block is accurately predicted by coefficients at the same position of its lowest gradient-direction adjacent blocks. Thirdly, the obtained DCT coefficient prediction errors are paired with a similar prediction-error pairing strategy to construct a two-dimensional prediction-error plane, and the secret data is imperceptibly embedded into the AC coefficients by using the two-dimensional histogram shifting algorithm. The performance of the proposed algorithm is evaluated on different datasets, and six representative images are involved in demonstrating its superiority. Extensive experimental results show that, compared to other state-of-the-art reversible data hiding algorithms, the proposed algorithm can achieve higher visual quality of the data-embedded image while maintaining low JPEG image file size increment. [ABSTRACT FROM AUTHOR]

This paper is concerned with designing an effective algorithm for recovering mismatched sparse signals from noise underdetermined measurements. The sparse recovery problem is formulated as the gradient optimization model of the residual cost function in a multidimensional continuous parameter space. The mismatched atoms of the discrete dictionary matrix are recovered as the accurate target sparse coefficient by solving a gradient descent algorithm with a hyperparameters joint adaptive adjustment function. To estimate the target parameters of frequency agile radar, we propose a gradient set matching pursuit algorithm. The algorithm uses the prior knowledge of resolution of the two-dimensional ambiguity function to build a discrete dictionary and obtains the support set according to the orthogonal matching pursuit principle. We divide the gradient set space in the neighborhood of the support set and calculate the mean gradient as the judgment direction and iteration step of the initial optimization process to overcome the residual function distortion and gradient error caused by random noise. The iterative convergence process of the proposed algorithm in the support set vector space is more robust, and the problem of falling into local optimum is avoided. Compared with the conventional sparse recovery algorithm and gradient descent algorithm, the proposed algorithm boosts its reconstruction performance of target parameters in the scenario of low SNR and mismatch of multiple scattering points. Simulation results show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Approximate joint diagonalization (AJD) of a set of target matrices is one of the approaches for blind identification, blind source separation (BSS), and blind source extraction. This approach is useful for applications with measurement noise and/or estimation errors. This paper presents an alternating least-squares (ALS) algorithm for solving AJD problem. The ALS algorithm consists of two processing phases. The first processing phase works for finding diagonal matrices by minimizing the indirect least-squares criterion. A mixing matrix is estimated by minimizing the constrained direct least-squares criterion in the second processing phase. Our ALS algorithm can circumvent numerically unstable behavior by improving the condition number of the estimated mixing matrix in the second processing phase. The proposed algorithm is shown through experimental results to have better estimation performance and faster convergence than existing ALS algorithms with lower computational requirement in the rectangular mixing case. • Approximate joint diagonalization (AJD), a technique of diagonalizing a set of target matrices, has been used widely for various applications. • A natural and common criterion for AJD is the least-squares (LS) approach. • This paper presents a repetitive improvement of the condition number of a matrix in the alternating least-squares (ALS) algorithm. • To implement this algorithm, an appropriate non-degenerate matrix is also picked as a replacement for the estimated ill-conditioned mixing matrix in ALS iterations. • We evaluate the behavior of the proposed algorithm in blind source separation and blind source extraction. [ABSTRACT FROM AUTHOR]

This paper presents a pattern synthesis algorithm achieving minimum mainlobe width via sparse optimization. The problem of synthesizing a pattern with minimum mainlobe width is formulated as a sparse optimization problem with l 0 norm by introducing a slack variable. To solve the sparse optimization problem, three existing relaxations for l 0 norm are presented. Moreover, a novel log-sum-exp penalty function is proposed to replace l 0 norm to be minimized in this paper, leading to a convex problem which can be solved directly and do not need to solve a sequence of sparse optimization problems compared with the iterative reweighted l 1 norm. Both the focused beam pattern and shaped beam pattern for arbitrary arrays can be synthesized utilizing the proposed algorithm. Besides, the mainlobe width of pattern synthesized by the proposed algorithm is minimum. Simultaneously, it brings an extra advantage that it is no longer necessary to accurately determine the mainlobe region without synthesis performance degradation. Numerical examples are presented to verify the effectiveness and superiority of the proposed algorithm. • Achieving minimum mainlobe width is formulated as sparse optimization problem with l 0 norm. • A log-sum-exp penalty function is proposed to replace l 0 norm for the sparse optimization problem. • The proposed method is suitable for not only focused beam pattern but also shaped beam pattern. • The proposed method is suitable for arbitrary arrays. • There is no need to accurately determine the mainlobe region. [ABSTRACT FROM AUTHOR]

Abstract This paper studies the stochastic behavior of the LMS algorithm in a system identification framework for a cyclostationary colored input without assuming a Gaussian distribution for the input. The input cyclostationary signal is modeled by a colored random process with periodically time-varying power. The generation of the colored non-Gaussian random process is parametrized in novel manner by passing a Gaussian random process through a coloring filter followed by a zero memory nonlinearity. The unknown system parameters are fixed in most of the cases studied here. Mathematical models are derived for the behavior of the mean and mean-square-deviation (MSD) and the excess mean-square error (EMSE) of the adaptive weights as a function of the input cyclostationarity. The models display the dependence of the algorithm upon the input nonlinearity and coloration. Three nonlinearities that are studied in detail with Monte Carlo simulations provide strong support for the theory. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a novel clutter suppression and ground moving target imaging approach is proposed for hypersonic vehicle (HSV) borne multichannel (MC) synthetic aperture radar (SAR) system. Compared with the traditional MC SAR ground moving target indication (GMTI) methods, the proposed algorithm can suppress clutter and decrease the moving target energy loss based on the improved MC clutter suppression method in the chirp Fourier transform (CFT) domain. Moreover, the moving target can be accurately focused by compensating the range curvature and the third order phase item. In addition, the motion parameters can be estimated precisely and the moving target can be focused accurately. Finally, the effectiveness of the proposed method is validated by the simulation examples. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates parameter estimation problems for multivariable controlled autoregressive autoregressive moving average (M-CARARMA) systems. In order to improve the performance of the standard multivariable generalized extended stochastic gradient (M-GESG) algorithm, we derive a partially coupled generalized extended stochastic gradient algorithm by using the auxiliary model. In particular, we divide the identification model into several subsystems based on the hierarchical identification principle and estimate the parameters using the coupled relationship between these subsystems. The simulation results show that the new algorithm can give more accurate parameter estimates of the M-CARARMA system than the M-GESG algorithm. [ABSTRACT FROM AUTHOR]

Abstract Analysis of functional magnetic resonance imaging (fMRI) data from multiple subjects is at the heart of many medical imaging studies, and recently, the approaches based on dictionary learning (DL) are noted as promising solutions to the problem. However, the DL-based methods for fMRI analysis proposed to date do not naturally extend to multi-subject analysis. In this paper, we propose a DL algorithm for multi-subject fMRI data analysis which is derived using a hybrid (temporal and spatial) concatenation scheme. It differs from existing DL methods in both its sparse coding and dictionary update stages. It has the advantage of learning a dictionary common to all subjects as well as a set of subject-specific dictionaries, as a result, it is able to generate both group-level spatial activation maps as well as group-level temporal dynamics, which are particularly attractive for task-based fMRI studies. In addition, by simultaneously learning multiple sub-specific dictionaries, it also provides us with unique sub-specific features as well. Performance of the proposed DL method is illustrated using simulated and real fMRI datasets. The results show that it can successfully extract common as well as sub-specific latent components. [ABSTRACT FROM AUTHOR]

Abstract The affine projection (AP) algorithm is one of the most celebrated adaptive filtering algorithms, and it achieves a good tradeoff between the convergence rate and computational complexity. However, the complexity of the AP algorithm increases with the projection order. A wealth of fast AP algorithms have been proposed to reduce the complexity in the last two decades. However, those low-complexity methods have not been well analyzed and compared. To fill this gap, this paper reviews the fast AP algorithms, including both fast filtering approaches and efficient solutions of the linear system of equations. The advantages and disadvantages of each fast implementation version are clarified based on an extensive performance evaluation, which could be useful to engineers when selecting a suitable algorithm for their specific applications and could also be a starting point for experts in this field to develop better solutions. [ABSTRACT FROM AUTHOR]

Abstract Extracting key frames from a video can reduce redundancies in continuous scenes and pithy represent the entire video. This technique copes with the issue of how to efficiently manage a large amount of video data and has many applications such as video indexing, querying, and browsing. Current key frame extraction methods often utilize sparse modeling, which assumes that each video frame can be expressed as a linear combination of a few representative key frames. Although these methods are successful, they are less aware of the fact that videos have spatio-temporal structure and used ℓ 1 norm based sparsity measure. In this paper, we propose a key frame extraction method that considers spatio-temporal information of videos. We employ a sparsity measure that is based on the determinant of the Gram matrix computed from entire video frames. The determinant sparsity measure is computed for the entire matrix, not for single frames, and thus it reflects the spatial or joint sparseness of the entire video. With this measure, the solutions are sparser than conventional sparseness measures but the cost function is non-convex and optimization becomes harder. Then, utilizing the fact that the proposed cost function is the difference of two convex functions, we employ an efficient algorithm based on the difference-of-convex (DC) programming, which often finds the global solution of the non-convex cost function. Experiments show that the proposed algorithm generates high-quality, high-compression key frames, compared with the existing key frame extraction method with the ℓ 1 norm. The determinant sparsity measure was recently proposed and can be utilized for more video processing applications such as video saliency detection. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes the theoretical foundations of a novel radar detection method, based on analysis and decision making in the moments space of the scattered signal parameters. Through an adaptive process and from large-size samples of these parameters, several normal-distributed moments are calculated, which allow assigning each resolution cell of the searching region to the background or anomaly classes. Following the Neyman–Pearson criterion two optimal detection algorithms are proposed, one for statistically independent moments and other for correlated moments. Finally, three background-anomaly pairs are simulated, taking the amplitude of the video signal as parameter and two of its moments for decision making. As result, the detection curves for two sizes of the sample are presented, showing the possibilities of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose a novel kernel adaptive filtering algorithm, which called variable learning rates kernel adaptive filter with single feedback (SF-VLRKAF). Based on a feedback structure, the past information can be used to estimate current output to improve the filtering performance, because of a momentum term existing in the weight update equation. A switch ON–OFF normalized variable learning rate is developed to obtain a tradeoff between convergence rate and filtering accuracy of the proposed algorithm. The weights, in the SF-VLRKAF, are updated at each iteration to avoid local minimum, and the analysis of mean square convergence is performed. Furthermore, a sufficient condition ensuring mean square convergence is obtained by applying the energy conservation relation. Moreover, we derive the lower and upper bounds on a theoretical result of the steady-state excess mean square error. Simulations for nonlinear regression, chaotic time-series predictions and real-world applications are presented to illustrate the effectiveness of the new algorithm. Graphical abstract Highlights • A KAF with single feedback and variable learning rates is designed. • The mean square convergence of proposed KAF is analyzed. • A sufficient condition for convergence of proposed KAF is obtained. • Simulation results show that the proposed KAF has superior performance. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a fast algorithm for sparse spectral estimation to achieve super-resolution SAR (synthetic aperture radar) sparse imaging of complex targets. The fast algorithm used the Newton method and fast Fourier transform (FFT) based preconditioned conjugate gradient (PCG) method to estimate sparse spectral parameters efficiently. Finally, a sparse SAR imaging was obtained based on sparse spectral parameters. It can suppress imaging sidelobe clutter and speckle with a super-resolution. The experiments with simulated and real targets were used to demonstrate the computational efficiency and super-resolution sparse imaging capability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we present an efficient algorithm for identifying and tracking the impulse response of a sparse system that exhibits time varying sparseness and is driven by correlated input. The proposed method convexly combines the outputs of two filters, namely, the sparsity unaware affine projection algorithm (APA), and the sparsity aware zero attracting affine projection algorithm (ZA-APA), each trying to identify the same system using the same input. The combining parameter is adapted by following a steepest descent of the error variance at the convex combination output. A detailed performance analysis of the proposed combination is carried out, which reveals that while for highly non-sparse and highly sparse systems, the proposed combination converges respectively to the APA and ZA-APA (i.e., better of the two filters under the given levels of sparsity), for certain sparsity ranges, it leads to a combination filter that performs better than both the constituent filters. The claims made are validated by exhaustive simulation studies using white, colored as well as speech inputs. [ABSTRACT FROM AUTHOR]

Highlights • This paper proposes a diffusion generalized maximum correntropy criterion. • The D-GMCC algorithm is adjusted for multitask estimation. • Mean and mean-square performance analysis of the D-GMCC algorithm are provided. Abstract Adopting mean-square error (MSE) criterion, distributed estimation algorithms achieve desirable performance if the background noise is drawn from the Gaussian distribution. However, these algorithms may degrade considerably in non-Gaussian situations, especially for the impulsive-noise. The diffusion generalized maximum correntropy criterion (D-GMCC) algorithm is proposed in this article to address this problem. Furthermore, we contribute in multiple tasks problem, which differs from single-task estimation problem. The relatedness of tasks among nodes is also studied to uncover its impact on estimation performance. Simulations illustrate that the proposed algorithm achieves desirable performance and outperforms other related methods. The results on the mean and mean square stability analysis of the proposed algorithm are also provided. [ABSTRACT FROM AUTHOR]