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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]

In contrast to the method based on motion equations, image registration proves effective for tracking moving objects in missions where establishing a motion model is unfeasible. However, in cases where the object is captured at varying imaging angles or against a similar background, a large number of outliers may arise during the process of image feature extraction. Consequently, when confronted with a large number of outliers, image registration methods exhibit a drawback in accurate outlier filtering, especially when spatial constraints on feature matching pairs are constrained. Ultimately, these factors can render object tracking inaccurate or even unattainable. In light of the aforementioned circumstances, this paper proposes TRESAC++, which based on a triplet relation feature matching method to effectively filter outliers. To enhance the operational efficiency of the algorithm, Principal Component Analysis is employed for dimensionality reduction, aiming to decrease the incidence of triple matches and subsequently mitigate execution time. To fortify the algorithm's robustness, a triplet relation linked with an initial data subset selection strategy is additionally proposed. Experimental results on five challenging experiments show that TRESAC++ rivals current state-of-the-art techniques in terms of computational cost, while the implementation exhibits better outlier filtering. Additionally, Experiment 4 and Experiment 5 use moving object datasets to confirm TRESAC++'s superior capability in precise object tracking. [ABSTRACT FROM AUTHOR]

Due to providing greater degrees-of-freedom (DOFs) and securer communication guarantees than traditional scalar array, polarized massive multiple-input multiple-output (MIMO) technique offers a prospective insight into millimeter-wave (mmWave) communication. In this paper, a compressed two-dimensional (2D) direction-of-arrival (DOA) and polarization estimation algorithm combining the compressive sampling propagator method with reduced-dimension multiple signal classification (CSPM-RDMUSIC) is developed for mmWave polarized massive MIMO systems. First, a synchronous compressive network is constructed to compress the high-dimensional output into a low-dimensional one. Thereafter, a propagator is structured to acquire the signal subspace. Subsequently, accurate 2D-DOA and polarization estimation are achieved using a coarse-refined strategy, where the vector cross-product technology is investigated for coarse 2D-DOA estimation and then RDMUSIC is exploited to perform 2D local search for accurate parameter estimation. Owing to integrating the CSPM framework with the RDMUSIC strategy, the developed approach provides high-precision parameter estimation with relative computational economy, supporting mmWave communication demands. What's more, it's suitable for arbitrary array geometry with high flexibility. Several experimental results validate the superiorities of the developed framework. • A synchronous compressive network is combined with propagator to compress high-dimensional data and avoid matrix decomposition. • A coarse-refined strategy using rotational invariance and RDMUSIC is explored for accurate 2D-DOA and polarization estimation. • The developed approach provides high-precision parameter estimation with relative computational economy. • The developed method is suitable for arbitrary geometry with high flexibility, supporting practical engineering applications. [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]

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]

A distributed network with highly colored input signal is often located in a scenario where the output signal is subject to impulse noise. Under the above scenario, the performance of the diffusion adaptive algorithms will suffer from performance degradation, which includes distributed parameter estimation accuracy and convergence/tracking rate. To solve these problems, the diffusion affine projection like maximum correntropy (DAPLMC) algorithm is proposed. Moreover, highly colored input signals tend to be mixed with noise, which will lead to biased estimation. To deal with the adverse impact of biased estimation, based on the bias compensation (BC) strategy, the bias-compensated DAPLMC (BC-DAPLMC) algorithm is proposed in this paper. By analyzing the convergence performance of the BC-DAPLMC algorithm, a range of its step size is obtained, and the steady-state error of the BC-DAPLMC algorithm is studied. The superior performance of the BC-DAPLMC algorithm and theoretical steady-state error analysis is validated by simulation results. [ABSTRACT FROM AUTHOR]

Content-aware image retargeting (CAIR) techniques are crucial in multimedia processing for displaying images on various devices while preserving visually salient contents with desirable visual effects. There are discrete and continuous algorithms. For the former, the artefacts happen when the foreground proportion is larger than the retargeting ratio; for the latter, the salient regions are prone to be squeezed. In this paper, we reformulate the retargeting process into sampling the salient signal and reconstruction under aesthetic supervision, the supervised multi-class image retargeting reconstruction (SMART) framework. The target images can be represented into complementary parts, the masked and unmasked ones, according to the saliency influences in the encoder phrase. The long-range sampling algorithm is proposed to calculate similarities through an 8-connected planar path while considering spatial distance and feature correlation. The sampled embeddings in latent space reconstruct the retargeted images under supervised signals for aesthetic quality. The semantic loss L s e m from the pretrained CLIP model can maintain consistency for both content and semantics. The supervised loss, L i r , is introduced to ensure the retargeted qualities are close to the preferred labels. Then, we release a new retargeting dataset comprising seven image classes (animal, building, car, flower, indoor, landscape and people) with supervised labels collected from designers for further aesthetic retargeting study. The ablation studies are conducted to confirm the effectiveness of the new dataset, and comparative experiments with state-of-the-art baselines demonstrate the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

This paper outlines a novel approach to the development of adaptive filtering algorithms, which minimizes susceptibility to the effects of non-Gaussian measurement noise. Using the generalized Gaussian density (GGD) function as the kernel function, we developed a generalized kernel risk-sensitive loss (GKRSL) criterion by which to derive the generalized minimum kernel risk-sensitive loss (GMKRSL) algorithm. We also developed a variable kernel width algorithm for GMKRSL (VKW-GMKRSL) to address the trade-off between convergence rate and steady-state misalignment resulting from a constant kernel width. In simulations, GMKRSL and VKW-GMKRSL outperformed existing works in system identification performance. [ABSTRACT FROM AUTHOR]

In Massive multiple-input multiple-output (MIMO) systems, channel estimation is crucial. The large size of the antennas causes a significant pilot and feedback overhead, making it challenging to estimate channels in massive MIMO systems. Besides, the studies have shown that mmWave channels are sparse due to the limited number of dominant propagation paths. Therefore, the motivation of this paper is to exploit the inherent sparsity of the massive mmWave MIMO channels to develop a semi-blind channel estimation with reduced number of pilots. To this goal, an expectation maximization (EM) based technique has been developed which leverages the sparsity of the underlying channel for its better estimation. An iterative approach is proposed to solve the modeled problem which simultaneously updates the channel coefficients and data symbols using available data and system structure at each iteration. The proposed method imposes sparsity with the aid of Smoothed L 0 norm (SL0) in the M-step. The simulation results demonstrate the proposed method have quick convergence and lower channel estimation error compared to the existing methods. As a quantitative evaluation, the proposed method attains the normalized mean square error of 9 × 10 − 2 and 7 × 10 − 3 at SNR = 5 dB and SNR = 15 dB , respectively. [ABSTRACT FROM AUTHOR]

The contribution of this paper is two-fold. Firstly, the well-known two-point Interpolated Discrete Fourier Transform (IpDFT) algorithm based on a Maximum Sidelobe Decay (MSD) window is generalized to the estimation of the frequency and the damping factor of a real-valued damped sinusoids. Analytical expressions for the detrimental contributions on the obtained estimators due to the image frequency component and wideband noise are then derived and their accuracies are verified through computer simulations. Secondly, leveraging on the derived expression, an improved version of the IpDFT algorithm is proposed which ensure more accurate estimates by compensating the contribution of the image frequency component. The performance of the improved IpDFT algorithm is compared with those provided by various state-of-the-art algorithms through meaningful computer simulations. [ABSTRACT FROM AUTHOR]

Accurate and fast vehicle detection is a key factor in the field of intelligent transportation. To solve the problems such as low recognition accuracy, slow detection speed, and poor robustness existing in current vehicle detection algorithms. This paper proposes a vehicle detection algorithm based on YOLOv5 and the coordinate attention mechanism (CA) named YOLO-CCS. YOLO-CCS enables the network to focus on the vehicle itself during the feature extraction process, reduces the loss of feature information and improves the effect of vehicle detection. In our algorithm, we firstly expand YOLOv5s with CA blocks at the location of feature extraction section of network backbone and enhance the extraction capabilities of key features, and suppress the interference of complex backgrounds by embedding position information. Secondly, to enable the network to extract rich feature information and improve the feature fusion capability of the network, we introduce the faster implementation of the Cross Stage Partial (CSP) Bottleneck with 2 convolutions (C2f) in the backbone and neck of the network, which has more residual blocks and skip connections, which provides rich feature semantic information, and heightens the feature extraction capabilities of the network. Thirdly, we integrate the SCYLLA-IOU (SIOU) loss function suitable for YOLO-CCS. So we can leverage the vector angle between the real box and the predicted box to further improve the accuracy of the algorithm and accelerate the convergence of the model. Experimental results show that, compared with the baseline model YOLOv5s, the m A P 50 and m A P 50 − 95 of our method increase by 3.2% and 1.7% respectively. Compared with other YOLO based models, m A P 50 has increased by approximately 4%. And the detecting speed of YOLO-CCS algorithm reaches 48 FPS, which can meet the real-time requirements of vehicle detection. [ABSTRACT FROM AUTHOR]

Tracking an unknown number of low-observable targets is notoriously challenging problem. This paper extends the multi-sensor multi-target tracking with generalized labeled multi-Bernoulli (GLMB) filter into the track-before-detect (TBD) observation model and presents its efficient implementation. The Gaussian belief propagation (BP) is applied into multi-sensor GLMB-TBD filter to address the computational bottleneck of computing GLMB filtering density for tracking multiple targets in challenging scenario. The Gaussian BP algorithm is used to directly compute marginal densities from the joint posterior density through the BP belief update wherein the GLMB filtering density at given time serves as prior information for next recursion. Compared to the Gibbs sampling-version implementation, the Gaussian BP-version implementation avoids the pruning of GLMB components without association information loss and the relevant association is preserved for BP belief update. Hence, the proposed Gaussian BP-version implementation can improve the tracking accuracy. Moreover, the computational complexity of our proposed Gaussian BP-version implementation scales linear complexity in the total number of measurements from all sensors. More importantly, the linear scaling in number of Bernoulli components for our proposed Gaussian BP-version implementation improves on the quadratic complexity exhibited by the Gibbs-version one. The experiment results show that the computational cost of the Gaussian BP-version implementation is significantly less than that of the Gibbs sampling-version implementation for the multi-sensor GLMB-TBD filter. [ABSTRACT FROM AUTHOR]

In a distributed sensor network, how to synthesize the target information and estimate the trajectories under the limitations for both the fields of view (FoVs) and the information channels is a challenging problem. Due to the difference between the FoVs of the sensors, the local multitarget densities can be different. The classical RFS-based fusion methods may discard the information that is only observed by partial sensors. Besides, the fusion method for the labeled RFS may encounter the label inconsistency sensitivity (LIS) problem. In this paper, we give the conditions for avoiding the LIS problem. Furthermore, the consensus information selector method is proposed for the distributed fusion problem with different local FoVs. At each time step, the local multitarget densities are converged within a finite number of iterations. The simulation results demonstrate that the theoretical conclusion is reasonable and the algorithm is effective. • The LIS problem can be avoided given the condition that all sensors share the same prior density. • The consensus information selector is proposed to solve the distributed fusion problem. • The consensus information selector is proved to converge within finite iterations. [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]

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]

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]

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]

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]

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]

Clustering algorithms strive to organize data into meaningful groups in an unsupervised fashion. For some datasets, these algorithms can provide important insights into the structure of the data and the relationships between the constituent items. Clustering analysis is applied in numerous fields, e.g., biology, economics, and computer vision. If the structure of the data changes over time, we need models and algorithms that can capture the time-varying characteristics and permit evolution of the clustering. Additional complications arise when we do not have the entire dataset but instead receive elements one-by-one. In the case of data streams, we would like to process the data online, sequentially maintaining an up-to-date clustering. In this paper, we focus on Bayesian topic models; although these were originally derived for processing collections of documents, they can be adapted to many kinds of data. The main purpose of the paper is to provide a tutorial description and survey of dynamic topic models that are suitable for online clustering algorithms, but we illustrate the modeling approach by introducing a novel algorithm that addresses the challenges of time-dependent clustering of streaming data. [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]

In this paper, the problem of terahertz pulsed imaging and reconstruction is addressed. It is assumed that an incomplete (subsampled) three dimensional THz data set has been acquired and the aim is to recover all missing samples. A sparsity-inducing approach is proposed for this purpose. First, a simple interpolation is applied to incomplete noisy data. Then, we propose a spatio-temporal dictionary learning method to obtain an appropriate sparse representation of data based on a joint sparse recovery algorithm. Then, using the sparse coefficients and the learned dictionary, the 3D data is effectively denoised by minimizing a simple cost function. We consider two types of terahertz data to evaluate the performance of the proposed approach: THz data acquired for a model sample with clear layered structures (e.g., a T-shape plastic sheet buried in a polythene pellet), and pharmaceutical tablet data (with low spatial resolution). The achieved signal-to-noise-ratio for reconstruction of T-shape data, from only 5% observation was 19 dB. Moreover, the accuracies of obtained thickness and depth measurements for pharmaceutical tablet data after reconstruction from 10% observation were 98.8%, and 99.9%, respectively. These results, along with chemical mapping analysis, presented at the end of this paper, confirm the accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Cubic spline interpolating the local maximal/minimal points is often employed to calculate the envelopes of a signal approximately. However, the undershoots occur frequently in the cubic spline envelopes. To improve them, in our previous paper we proposed a new envelope algorithm, which is an iterative process by using the Monotone Piecewise Cubic Interpolation. Experiments show very satisfying results. But the theoretical analysis on why and how it works well was not given there. This paper establishes the theoretical foundation for the algorithm. We will study the structure of undershoots, prove rigorously that the algorithm converges to an envelope without undershoots with exponential rate of convergence, which can be used to determine the number of iterations needed in the algorithm for a good envelope in applications. [ABSTRACT FROM AUTHOR]

The instantaneous frequency (IF) is a very important feature of nonstationary signals in numerous applications. The first overview of the concept and application of the IF estimators is presented in seminal papers by Boashash. Since then, a significant knowledge has been gained about the performance of the IF estimators. This knowledge has been used not only for development of various IF estimators but also for introduction of novel time–frequency (TF) representations. The IF estimation in environments characterized by low signal-to-noise (SNR) has achieved significant benefits from these theoretical developments. In this paper, we review some of the most important developments in the last two decades related to the concept of the IF, performance analysis of IF estimators, and development of IF estimators for low SNR environments. [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]

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]

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 recent years, the distributed estimation problem based on the diffusion strategy has received more and more attention, where the node filters cooperate with other in-network node filters to estimate the unknown parameter vector. In this paper, we propose a Diffusion Bias Compensated Recursive Maximum Correntropy Criterion (DBCRMCC) algorithm based on the idea of distributed estimation for adaptive filtering in network containing input noise and non-Gaussian output noise. The Recursive Maximum Correntropy Criterion (RMCC) is an adaptive filtering algorithm with Maximum Correntropy Criterion (MCC) as the cost function which is robust to large outliers. Considering that the estimates directly obtained by the RMCC algorithm are biased when the input is disturbed by noise, this paper proposes the DBCRMCC algorithm under some reasonable assumptions by using the principle of unbiased estimation and combining it with a diffusion strategy. Through bias-compensation for biased estimates and cooperation among node filters, the asymptotic unbiased estimates of the unknown parameters can be obtained. The simulation results show that the proposed DBCRMCC algorithm has acceptable convergence speed and estimation accuracy in the environment where the input signal is noisy and the output noise is non-Gaussian. [ABSTRACT FROM AUTHOR]

The accuracy of target position and velocity measurement is one of the most concerned indexes of Distributed High Frequency Surface Wave Radar (HFSWR). The target localization of HFSWR is commonly achieved by indirect positioning approach. Recently, some direct positioning methods using the raw signals collected on all base-stations have been studied, which have superior positioning performance compared with indirect approaches. But there is almost no joint estimation method for target position and vector-velocity. In this paper, a novel direct target localization and vector-velocity measurement method based on bandwidth synthesis (BS-DTL) in distributed HFSWR system is investigated to improve the accuracy of the target. BS-DTL algorithm that makes full use of the information of all base-stations is considered for the joint estimation of target position and vector-velocity. This paper studies the detection and estimation performance of the BS-DTL algorithm and traditional direct target localization method (DTL). Numerical simulation results show that the proposed algorithm outperforms the traditional DTL method and the position and vector-velocity can be accurately estimated. [ABSTRACT FROM AUTHOR]

Meter-wave MIMO radar has multipath effects in the low elevation angle area, seriously affecting the target elevation estimation performance. Therefore, the mainstream height measurement methods are generalized MUSIC and maximum likelihood estimation algorithms that do not require decoherence processing. Still, their complexity is high, and their performance is poor at low signal-to-noise ratios. Compressed sensing and time-reversal have been applied in height measurement, with good accuracy but high complexity. To quickly and accurately obtain the height parameters of the target, this paper proposes a low-elevation height measurement method for meter-wave MIMO radar based on adaptive beamforming. First, we analyze and simplify the signal model; then use the channel matching matrix to eliminate the influence of the reflected wave on the direct wave; then use the target direct wave direction and the reflected wave direction as the interference direction, and perform adaptive beamforming on it to obtain the target elevation angle value. Furthermore, the dimension reduction matrix is used to reduce the dimensionality of the received data signal. Then an algorithm suitable for undulating terrain and inclined ground is proposed and simulated. In addition, this paper gives the complexity of the proposed algorithm and the comparison algorithm. At last, in terms of the signal-to-noise ratio, the number of snapshots, the number of array elements, and the elevation angle, many simulation experiments are carried out to compare the proposed height measurement algorithm with the mainstream height measurement algorithm. The simulation results show that the accuracy and robustness of the proposed height measurement algorithm are the best under these factors. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the implementation of two hardware architectures, i.e., A2 Lattice Vector Quantization (LVQ) and Multistage A2LVQ (MA2LVQ), using a Field-Programmable Gate Array (FPGA). First, the renowned LVQ quantizer by Conway and Sloane is implemented followed by a low-complexity A2LVQ based on a new A2LVQ algorithm. It is revealed that the implementation requires high number of multiplier circuits. Then the implementation of a low-complexity A2LVQ is presented. This implementation uses only the first quadrant of the A2 lattice Voronoi region formed by W and T regions. This paper also presents the implementation of a multistage A2LVQ (MA2LVQ) with an architecture built from successive A2 quantizer blocks. Synthesis results show that the execution time of the low-complexity A2LVQ reaches up to 35.97 ns. The MA2LVQ is implemented using both low-complexity A2LVQ and ordinary A2 architectures. The system with the former architecture utilizes less logic and register elements by 47%. [Copyright &y& Elsevier]

Abstract: Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varriʼs and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm. [Copyright &y& Elsevier]