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Abstract: In this paper a variant of the set partitioned embedded block coder (SPECK) image compression called listless SPECK (LSK) is presented. LSK operates without lists and is suitable for a fast, simple hardware implementation. LSK has a fixed predetermined memory requirement about 50% larger than needed for the independent image. Instead of lists, a state table with two bits per coefficient is used to keep track of the block coding and the information that has been encoded. LSK sparsely marks selected block nodes of insignificant blocks in the state table, in such a way that large groups of predictably insignificant pixels are easily identified and skipped during the coding process. The image data is stored as a one-dimensional recursive zigzag array for computational efficiency and algorithmic simplicity. Performance of the proposed algorithm on standard test images is nearly same as SPECK. [Copyright &y& Elsevier]

Abstract: The weighted least squares (WLS) algorithm has proven useful for modern positron emission tomography (PET) scanners to approach reconstructions with non-Poisson precorrected measurement data. In this paper, we propose a new time recursive sequential WLS algorithm whose derivation uses the time-varying property of data acquisition of PET scanning. It ties close relationship with the time-varying Kalman filtering and can be extended appropriately to an iteration fashion as the absence of proper a priori initializations. The performance of sequential WLS is evaluated experimentally. The results show its fast convergence over both the multiplicative and coordinate-based iterative WLS methods. It also produces relative uniform estimate variances that makes it more suitable for routine applications. [Copyright &y& Elsevier]

Abstract: The spectral analysis of regularly-sampled (RS) data is a well-established topic, and many useful methods are available for performing it under different sets of conditions. The same cannot be said about the spectral analysis of irregularly-sampled (IS) data: despite a plethora of published works on this topic, the choice of a spectral analysis method for IS data is essentially limited, on either technical or computational grounds, to the periodogram and its variations. In our opinion this situation is far from satisfactory, given the importance of the spectral analysis of IS data for a considerable number of applications in such diverse fields as engineering, biomedicine, economics, astronomy, seismology, and physics, to name a few. In this paper we introduce a number of IS data approaches that parallel the methods most commonly used for spectral analysis of RS data: the periodogram (PER), the Capon method (CAP), the multiple-signal characterization method (MUSIC), and the estimation of signal parameters via rotational invariance technique (ESPRIT). The proposed IS methods are as simple as their RS counterparts, both conceptually and computationally. In particular, the fast algorithms derived for the implementation of the RS data methods can be used mutatis mutandis to implement the proposed parallel IS methods. Moreover, the expected performance-based ranking of the IS methods is the same as that of the parallel RS methods: all of them perform similarly on data consisting of well-separated sinusoids in noise, MUSIC and ESPRIT outperform the other methods in the case of closely-spaced sinusoids in white noise, and CAP outperforms PER for data whose spectrum has a small-to-medium dynamic range (MUSIC and ESPRIT should not be used in the latter case). [Copyright &y& Elsevier]

Abstract: In this paper we propose a new algorithm for performing blind source separation and signal recovery for the narrowband case. This algorithm seeks to estimate and implement the Wiener filter directly, using the second- and fourth-order cumulants. The estimated filter is asymptotically consistent with or without noise, and is not subject to the bound imposed by pre-whitening as with some existing algorithms. In many cases it nearly achieves the clairvoyant Wiener limit as performance studies show. [Copyright &y& Elsevier]

Abstract: This paper presents an algorithm for estimating the parameters of multicomponent chirp signals. The estimator is based on the cubic phase function (CPF), which is efficient to estimate the parameters of monocomponent first-, second-, and third-order polynomial phase signal. When the CPF is dealing with multicomponent chirp signals, the spurious peaks arise and hence the identifiability problem exists. A new approach based on the product cubic phase function (PCPF) is proposed to remove the identifiability problem. The occurrence probability of spurious peak, and the effect of noise to the estimator are statistically studied. The simulation examples are provided for validating the theoretical analysis. [Copyright &y& Elsevier]

Abstract: Interference can be a significant problem in loosely organized networks such as ad-hoc radio networks. One technique for dealing with interference is to attempt joint detection of all signals arriving at a node. Iterative algorithms based on the turbo-principle yield significant performance improvements when applied to this problem. This paper concentrates on performance analysis of iterative interference cancellation for forward-error correction coded signals in the presence of Rayleigh fading. The variance transfer analysis of Alexander et al. [European Trans. Telecommun. 9 (5) (1998) 419–426] is generalized for unequal user powers and codes. A surprising result is that the received power variation due to fading can assist convergence. [Copyright &y& Elsevier]

Abstract: This paper proposes a novel formulation of the generalized eigendecomposition (GED) approach to blind source separation (BSS) problems. The generalized eigendecomposition algorithms consider the estimation of a pair of correlation matrices (a matrix pencil) using observed sensor signals. Each of various algorithms proposed in the literature uses a different approach to form the pencil. This study proposes a linear algebra formulation which exploits the definition of congruent matrix pencils and shows that the solution and its constraints are independent of the way the matrix pencil is computed. Also an iterative eigendecomposition algorithm, that updates separation parameters on a sample-by-sample basis, is developed. It comprises of: (1) performing standard eigendecompositions based on power and deflation techniques; (2) computing a transformation matrix using spectral factorization. Another issue discussed in this work is the influence of the length of the data segment used to estimate the pencil. The algorithm is applied to artificially mixed audio data and it is shown that the separation performance depends on the eigenvalue spread. The latter varies with the number of samples used to estimate the eigenvalues. [Copyright &y& Elsevier]

Abstract: Over the years ElectroCardioGram (ECG) signal has been used to assess the cardiovascular condition of humans. In practice, real time acquisition and transmission of the ECG may contain noise signals superimposed on it. In general, the signal processing algorithms employed for denoising provide optimal performance and eliminate the high frequency noise between any two beats contained in a continuous ECG signal. Despite their optimal performance, the signal processing algorithms significantly attenuate the peaks of characteristics wave of the ECG signal. This paper presents a selection procedure of mother wavelet basis functions applied for denoising of the ECG signal in wavelet domain while retaining the signal peaks close to their full amplitude. The obtained wavelet based denoised ECG signals retain the necessary diagnostics information contained in the original ECG signal. [Copyright &y& Elsevier]

Abstract: In this paper, a novel analytic technique is presented for the design of finite impulse response digital filters with simultaneous amplitude and phase requirements. The design process relies on the interpolation concept and is based on the generation of arbitrary amplitude and phase polynomials. The new class of FIR filters lays in between the exact linear phase filters at all frequencies and the minimum phase FIR filters based on the amplitude approximation only. It interpolates arbitrary (e.g., linear) phase response in the passband and arbitrary amplitude characteristics in pass- and stopbands. The introduced class of filters can be used on the amplitude and phase equalization of both FIR and IIR digital filters. Illustrative examples are included in order to investigate the main properties of these filters and to demonstrate that the proposed design method leads to a significant saving in the arithmetic computations. [Copyright &y& Elsevier]

Abstract: Simple linear independent component analysis (ICA) algorithms work efficiently only in linear mixing environments. Whereas, a nonlinear ICA model, which is more complicated, would be more practical for general applications as it can work with both linear and nonlinear mixtures. In this paper, we introduce a novel method for nonlinear ICA problem. The proposed method follows the post nonlinear approach to model the mixtures, and exploits the difference between a linear mixture and a nonlinear one from their nature of distributions in a multidimensional space to develop a separation scheme. The nonlinear mixture is represented by a nonlinear surface while the linear mixture is represented by a plane. A geometric learning algorithm named as post nonlinear geometric ICA (pnGICA) is developed by geometrically transforming the nonlinear surface to a plane, i.e., to a linear mixture. Computer simulations of the algorithm provide promising performance on different data sets. [Copyright &y& Elsevier]

Range data is very important in human-computer interaction applications. Although less costly, range acquisition and processing still presents a speed vs data reliability tradeoff. This paper proposes a method that, given noisy and generally unreliable range data, can filter out erroneous information using range histograms for regions of interest selected in registered color data. Then, using the resulting consistent data that has passed filters, this method limits the depth search space dynamically using motion history and its current state. Experimental results demonstrate the success of the proposed algorithm. Using filtered range data, the algorithm correctly identified the hand involved in manipulation 99.85% of the time. Dynamic disparity adjustment produced a speedup of 60.17% over a static disparity range selection. An application to virtual reality navigation is also presented. [Copyright &y& Elsevier]

This paper studies image fusion techniques in the discrete cosine transform (DCT) domain. A new image fusion technique based on a contrast measure defined in the DCT domain is presented. The performance of our contrast measure based technique is analyzed and compared with other image fusion techniques. Experimental results show that there is no difference in visual quality between the fused image obtained by our algorithm and that obtained by a wavelet transform based image fusion technique. But because our algorithm is carried out in the DCT domain, it is time-saving and simpler when the fused image needs to be saved or transmitted in JPEG format or when the images to be fused were saved in JPEG format. [Copyright &y& Elsevier]

Sparsity of transform domain coefficients is a critical requirement for algorithms employing wavelet transforms to pre-process signals or images. This paper examines a measure of transform domain sparsity which provides a means of comparing wavelet and subband decomposition performance. The transforms of one-dimensional signals and two-dimensional images are considered using 110 different wavelets and nonlinear subband decomposition filters, including weighted order statistics and functions of weighted order statistics. These measurements indicate that nonlinear subband decompositions employing median filters or central order statistics can produce sparser decompositions than wavelets for certain classes of signals and images. [Copyright &y& Elsevier]

The paper investigates blind finite-impulse-response (FIR) equalization schemes for quadrature amplitude modulation signalling. We compare a bootstrap maximum a posteriori probability (MAP) equalizer with a recently introduced concurrent constant modulus algorithm (CMA) and decision directed (DD) equalizer (CMA+DD). Both equalizers are known to outperform the CMA considerably in the situations where carrier recovery is performed and the signal constellation is known. The concurrent CMA+DD equalizer has a complexity that is slightly more than twice of the CMA, and the bootstrap MAP equalizer has computational requirements that are only slightly more complex than the CMA. Simulation results indicate that the bootstrap MAP blind FIR equalizer has a faster convergence rate and better steady-state performance than the concurrent CMA+DD blind FIR equalizer, but tuning of the former is more complicated than the latter. [Copyright &y& Elsevier]

The compact representation of integers is an important consideration in areas such as data compression. In this paper a novel, simple, and effective approach for lossless compression of large-dynamic-range integer sequences is presented. It is shown that the proposed code is better than other codes for stepwise distributions. An optimization method for finding the optimal coding parameters is described. The method links the optimization to curve fitting. Because of the simplicity of the coding scheme, the data compression algorithms can be implemented using hardware circuitry to meet the requirements of real-time applications. [Copyright &y& Elsevier]

This paper provides an overview of the basic concepts, current state and future directions of uniform and nonuniform multirate filter banks and their applications. Research in the areas of multidimensional filter banks, and wavelet transforms, as a direct extension to the topic is still proceeding at a rapid rate. Different design techniques and algorithms that are currently of interest are investigated from hardware complexity and reconstructed signals'' quality points of view. Important similarities and differences between different filters categories are noted, which lead to a discussion of important issues that need to be resolved. Distortions and quantization effects present in the filter banks are reviewed and some solutions for controlling different types of distortions are introduced. The problem of designing QMF banks that can provide good frequency resolution while allowing for exact or near perfect reconstruction of the signal is quite challenging, because so many dissimilar types of distortion must be minimized and/or eliminated in the same design context. Recently, the perfect reconstruction cosine-modulated filter bank and filter banks with block sampling have emerged as an attractive choice of filter bank with respect to implementation complexities and design cost. New trends and future directions including selective multirate filter banks, filter banks with nonuniform decimation ratio, filter banks with discrete coefficients, multidimensional filter banks, nonlinear filter banks and data dependent decompositions are presented. Finally, some selected applications of multirate filter banks are described; namely transmultiplexers, speech and image coding, analog voice privacy systems and wavelets based signal and image compression. [Copyright &y& Elsevier]

Britton, D. F., and Smith, M. J. T., Generalized Gaussian Decompositions for SAR Enhancement Using Analysis-by-Synthesis, Digital Signal Processing12 (2002) 284–294Quality enhancement algorithms for synthetic aperture radar (SAR) images offer the potential to increase the accuracy of analysis and threat assessment. This paper presents a new SAR image enhancement method for that purpose. The method is built around a Gaussian representation of the image using analysis-by-synthesis as the engine for the approximation. Its sequential structure and synthesis property enables object boundaries and speckle characteristics to be decoupled and modified to enhance the quality. Evaluations demonstrate that the algorithm helps improve the performance of automatic target recognition systems as well as enhance subjective appearance. [Copyright &y& Elsevier]

In this paper, a novel direction of arrival (DOA) estimation algorithm is proposed for monostatic multiple-input multiple-output (MIMO) radar. Due to redundancy of the received data of monostatic MIMO radar, the computational complexity of the inversion of the covariance matrix is increased. In order to reduce the large computational complexity of existing subspace-based algorithms, we perform two reduced-dimensional operations. First, the high-dimensional received data is transformed the low-dimensional received data through a special transformation matrix. Second, the low-dimensional received data is transformed into beamspace. The receive beamspace filter is designed using convex optimization, and it can flexibly control the bandwidth and limit the sidelobe level. To ensure that the converted noise is Gaussian white noise, the prewhitening process is performed. Based on the above precondition, the reduced-dimensional beamspace technique can be effectively combined with the unitary ESPRIT model. Finally, the Cramer-Rao bound (CRB) on angle estimation in element space and reduced-dimensional beamspace is calculated for performance analysis. Numerical simulations verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

With the wide application of simple image editing software, forgery images have become severe social problems with extremely damaging effects. The copy-move forgery technique is commonly used to temper an image by copying some regions of an image and pasting them somewhere in the same image. In this paper, a novel algorithm is proposed for the detection of copy-move forgery. First, the keypoints of the input image are computed using a keypoint extraction algorithm. Second, a keypoint-matching algorithm is used to match similar keypoints as the candidate keypoint pairs. Third, a proposed novel Two-Stage Filtering algorithm, including the Grid-Based Filter and the Clustering-Based Filter, is applied to filter out most of the false matching keypoint pairs. Subsequently, image matting is achieved by the Delaunay triangulation algorithm so that the marked areas indicate the forgery regions. Three copy-move forgery datasets are used to compare the performances of the proposed algorithm with some state-of-the-art algorithms. The experimental results demonstrate that the proposed algorithm's overall performance is superior to other solutions for detecting copy-move forgery images. [ABSTRACT FROM AUTHOR]

Transform-based block digital filtering (BDF) is a powerful tool for reducing computational complexity and increasing the parallelism of digital filtering systems. Most commonly used transforms, such as the discrete Fourier transform (DFT) and the discrete cosine transform (DCT), are not suitable for fixed-point implementation since they lead to a large quantization error. Otherwise, Fermat number transform (FNT) ensures an error-free computation in addition to a lower computational cost. In this paper, we propose an efficient algorithm for the optimal FNT-BDF design based on a quadratic criterion. A significant reduction in the computational cost of the algorithm is achieved through the use of the properties of circulant matrices and the Babai estimate method, used to solve integer least squares problem. Compared to DFT-BDF design, simulation results verify that our optimal FNT-BDF is more efficient and more accurate. [ABSTRACT FROM AUTHOR]

This paper aims to develop an accurate stochastic model for the diffusion normalized least-mean-square (dNLMS) algorithm operating with both combine-then-adapt (CTA) and adapt-then-combine (ATC) strategies, aiming to provide a theoretical basis for supporting the study of this algorithm. In particular, considering uncorrelated and correlated Gaussian input data, model expressions are derived for predicting the mean and mean-square behavior of either an individual node or the whole adaptive network for both transient and steady-state phases. Based on these expressions, the impact of the diffusion strategy along with a combination rule on the algorithm performance is assessed and discussed. In addition, examples are presented to demonstrate how model expressions can help the designer in the adjustment of the algorithm parameters without the need of extensive trial-and-error procedures, making performance comparisons less laborious. The effectiveness of the proposed model is assessed through simulation results covering different operating conditions, network topologies, combination rules, step-size values, as well as for a wide range of eigenvalue spreads of the input data correlation matrix and signal-to-noise ratio (SNR) values. [ABSTRACT FROM AUTHOR]

The frequency diverse array multiple-input multiple-output (FDA-MIMO) radar can detect target range by exploiting a small frequency offset across the transmit sensors, which can be utilized to jointly estimate angle and range. Nevertheless, the detection performance is basically restricted by the array aperture and signal bandwidth. In this paper, we propose a joint optimization design scheme for FDA-MIMO radar, i.e., the virtual coprime planar array with 'unfolded' coprime frequency offsets (VCPA-UCFO) framework, for 3D localization without ambiguity. The VCPA-UCFO framework can significantly save sensors and physical space while remarkably extend array aperture and signal bandwidth, which brings about remarkably enhanced economic benefits and estimation performance. Additionally, we construct the 3D localization problem as 3D-MUSIC spatial spectrum function and transform the 3D total spectrum search of the conventional 3D-MUSIC algorithm into 1D local spectrum search by cooperating ESPRIT algorithm and twice reduce dimension MUSIC (TRD-MUSIC) algorithm. The TRD-MUSIC algorithm can significantly relieve computational burden but with no performance degradation. The CRBs are given as performance benchmark. The analysis and simulations have verified the effectiveness and advantages of VCPA-UCFO framework and TRD-MUSIC algorithm in system cost, localization accuracy, resolution and computational complexity. [ABSTRACT FROM AUTHOR]

Two-dimensional (2-D) direction of arrival (DOA) estimation exploiting interlaced uniform planar array (IUPA) motion is discussed in this paper, and a Discrete Fourier Transform cascading Taylor Expansion (DFT-TE) algorithm is proposed. Specifically, the proposed IUPA structure possesses larger inter-element spacing than traditional uniform planar array (UPA), and it can mitigate the mutual coupling effectively. Simultaneously, the proposed IUPA structure is suitable for array motion. The synthetic IUPA generated by IUPA motion can offer more available array sensors and better DOA estimation performance than synthetic UPA and synthetic sparse uniform planar array (SUPA). Furthermore, considering the high computational complexity of 2-D DOA estimation, we propose the DFT-TE method. The DFT-TE method requires neither eigenvalue decomposition nor search operation. It gets the initial angles by employing the direct DFT method, and obtains the offset compensation for refined estimates by utilizing the Taylor Expansion method and total least squares (TLS) criterion. The proposed method has lower complexity and better estimation performance than the traditional 2-D DFT cascading search (DFT-S) method. Simulation results verify the effectiveness and advantages of the IUPA structure and DFT-TE method. [ABSTRACT FROM AUTHOR]

Unimodular/Phase-only sequence having impulse like aperiodic auto-correlation function plays a central role in the applications of RADAR, SONAR, Cryptography, and Wireless (CDMA) Communication Systems. In this paper, we propose a fast iterative algorithm to design phase-only sequences of arbitrary lengths by minimizing the Integrated Side-lobe Level (ISL) metric, which is very closely related to the auto-correlation property of a sequence. The ISL minimization problem is solved iteratively by using the Majorization-Minimization (MM) technique, which ensures a monotonic convergence to the stationary minimum point. To highlight the performance of a proposed algorithm, we conduct the numerical experiments for different sequence lengths using different initializations and also compare them with the existing algorithms. Numerical simulations show that irrespective of the sequence length and initialization, the proposed algorithm is performing better than the state-of-the-art algorithms in terms of speed of convergence. We also show a computationally efficient way to implement our proposed algorithm by using the FFT and IFFT operations. [ABSTRACT FROM AUTHOR]

In this paper, an iteration algorithm based on interference iteration is proposed and referred to as I-PMF. Derived from the parametric matched filter (PMF), I-PMF has a low computational cost and reveals not only an iteration relationship between the autoregressive (AR) coefficient matrices and interferences in mathematics, but also a mechanism for PMF in suppressing interferences. Then the iteration of I-PMF is utilized in an adaptive method, namely I-PAMF. The average minimum output signal-to-interference-and-noise ratio (SINR) of I-PAMF is analyzed with separated interferences and verified by numerical results. It is shown that the computational cost of the proposed iterative algorithm, i.e. I-PMF, is low and the detection performance of the proposed adaptive iterative algorithm, i.e. I-PAMF, outperforms most of its counterparts with small training size. Although there might be a small loss of detection performance for I-PAMF compared with a few of its counterparts in some cases, I-PAMF still has a much lower computational cost than these counterparts. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive algorithm is derived by considering that the beamforming vector can be decomposed as a Kronecker product of two smaller vectors. Such a decomposition leads to a joint optimization problem, which is then solved by using an alternating optimization strategy along with the steepest-descent method. The resulting algorithm, termed here Kronecker product constrained least-mean-square (KCLMS) algorithm, exhibits (in comparison to the well-known CLMS) improved convergence speed and reduced computational complexity; especially, for arrays with a large number of antennas. Simulation results are shown aiming to confirm the robustness of the proposed algorithm under different operating conditions. [ABSTRACT FROM AUTHOR]

In this paper a new algorithm for direction of arrival (DOA) estimation in coprime arrays based on the autocorrelation domain (AD) method is proposed. The spatial autocorrelation sequence of the virtual uniform linear array (ULA) associated with the coprime array is built by averaging the diagonals of the spatially smoothed covariance matrix. The AD method estimates the DOAs by solving a generalized eigenvalue problem and refining the obtained solution. The main advantage of the introduced method is a spatial spectrum without false peaks. Simulation results indicate that the proposed approach is better than existing methods, especially for small number of snapshots. [ABSTRACT FROM AUTHOR]

In this paper, a one-step method is proposed for the classification and estimation of the parameters in the scenario of mixed near-field and far-field stationary sources. By constructing several temporal-spatial cumulant matrices, utilizing the stationary property of sources, and introducing a mechanism for controlling the rank, a parameters estimation matrix is formed. This matrix contains electric angles and signals information. By introducing a method based on the rotational invariant technique and using the estimation signal parameter, the direction-of-arrival of all sources' signals and the range of the near-field sources (NFSs) are estimated. Also, a reasonable classification of the signals types is provided. Unlike conventional methods, the proposed method does not require any spectral search stage, so it reduces the computational complexity to a great extent. The proposed algorithm prevents the array aperture loss, has no need to know the number of NFSs or field-field sources, and does not require any extra stage to pair the parameters. Mathematical analysis and simulation results validate the good performance of parameters estimation and sources classification in the proposed method. [ABSTRACT FROM AUTHOR]

Different transmit antenna elements in a frequency diverse array (FDA) radar transmit different frequencies. This makes the transmitted beam to continuously change its orientation, and enables an FDA to scan the space around itself in a simple manner. Furthermore, an FDA can find the range as well as the direction of each target simultaneously. The achievable resolution of direction estimation depends on the receive array geometry. The range resolution can be enhanced by decreasing the width of transmit beam, which depends on the transmit array geometry. The later property makes FDA especially attractive because it is no longer necessary to produce sharp, wideband, and high power pulses to achieve high range resolution. FDA has been studied mostly for collocated transmit and receive array, and a very little has been done on its application in bistatic radar. In this paper we propose a new signal transmission scheme for bistatic FDA radar, and demonstrate how one can detect and localize the targets accurately using a computationally efficient algorithm. The proposed signal transmission scheme is designed to produce the narrowest transmit beam for a given number of transmit antennas. This achieves significantly better detection and localization performance compared to the existing methods. Our estimation approach uses a fast algorithm to produce some initial estimates that can be used to initialize a numerical algorithm for computing the maximum likelihood estimates. We also derive the Cramér Rao bound (CRB) associated with the estimation problem, and demonstrate that the variances of the estimates produced by the proposed method are close to corresponding CRBs. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of two-dimensional (2-D) direction of arrival (DOA) estimation for L-shaped monostatic MIMO radar with two-level nested linear array (NLA). To obtain more degrees of freedom (DOFs) than traditional nested L-shaped (NLs) MIMO radar, we propose the extensional NLs (ENLs) MIMO radar by extending all of the sensor positions in transmitter array with an identical magnification compared with NLs MIMO radar. The ENLs MIMO radar can offer O (M 1 M 2 N 1 N 2) DOFs with only O (M 1 + M 2 + N 1 + N 2) sensors, where M 1 (N 1) and M 2 (N 2) respectively represents the numbers of the first-level and second-level sensors of NLA in transmitter (receiver) array. Furthermore, to avoid the eigenvalue decomposition, spatial spectral search and polynomial root finding technique in spatial smoothing multiple signals classification (SS-MUSIC) and SS-ROOT-MUSIC, and to improve the estimation accuracy simultaneously, we propose a search-free iterative Taylor expansion (SF-ITE) algorithm to perform DOA estimation, which employs the Discrete Fourier transform (DFT) method to get the coarse estimation, and then utilizes the iterative Taylor expansion technique to obtain fine estimation. SF-ITE can get more precise estimation by performing iteration only once than both SS-MUSIC and SS-ROOT-MUSIC. In addition, we derive an angle-pairing procedure to help to eventually resolve more targets than the total number of sensors. Finally, simulation results are provided to validate the superiority of the ENLs MIMO radar and SF-ITE algorithm. [ABSTRACT FROM AUTHOR]

Compared with the traditional subspace methods, the application of compressed sensing (CS) to the direction-of-departure (DOD) and direction-of-arrival (DOA) estimation in bistatic multiple-input multiple-output (MIMO) radar can achieve robustness to coherent targets and high localization accuracy with a limited number of snapshots. However, the performance of grid-based CS reconstruction methods degrades since there is an unavoidable basis mismatch between the actual DOD/DOA and the assumed basis. As a gridless CS method, the atomic norm minimization (ANM) has attracted much attention. Specifically, the use of multiple snapshots in ANM has improved the parameter estimation accuracy in contrast with a single snapshot. In this paper, we address the problem of gridless DOD and DOA estimation in bistatic MIMO radar, and develop a multiple-snapshot 2D-ANM algorithm and its two low complexity versions. We start with a rigorous derivation on how to convert the multiple-snapshot 2D-ANM into a semi-definite programming problem, and then explore its dual problem. To overcome the heavy computational burden of the 2D-ANM when the numbers of snapshots and array elements increase, we further propose a 2D-ANM algorithm with snapshot reduction (2D-ANM-SR), as well as an improved 2D-ANM-SR algorithm based on alternating direction method of multipliers (2D-ANM-SR-ADMM). Numerical examples show that with similar accuracy in comparison to 2D-ANM, much lower computational complexity can be achieved via 2D-ANM-SR and 2D-ANM-SR-ADMM. [ABSTRACT FROM AUTHOR]

Estimation of haplotype sequences from DNA sequencing samples is a challenging task whose mathematical formulation leads to an NP-hard problem. Also, accuracy of the estimates plays an essential role in providing the required information for personalized medicine. In order to fully incorporate the available quality of measurements with higher accuracy into the estimates, in this paper, we propose a new optimization design using a weighted version of the well-established matrix completion approach. This is performed by penalizing the difference between the measurements and the desired matrix using some weights, which are used to form an optimization constraint. Accordingly, we derive the corresponding error bound for the desired matrix, which shows that a larger noise power increases the estimation error with a factor proportional to the inverse of the mentioned weights. This leads to devising a new algorithm called the Haplotype reconstruction using nuclear norm minimization with Weighted Constraint (HapWeC). Computer simulations show the outperformance of the HapWeC compared to some recent algorithms in terms of the normalized reconstruction error and reconstruction rate. [ABSTRACT FROM AUTHOR]

This paper focuses on the weak maneuvering target detection problem in the alpha-stable noise (ASN) environment. A novel coherent integration framework is proposed, where the range migration (RM) is first removed via approximate linear methods to make the noise distribution character remain unchanged, and then the ASN suppression, Doppler frequency migration (DFM) compensation, and coherent integration are simultaneously achieved in the proper transform domain. Based on this framework, a robust detection method is developed. First, the second-order keystone transform (SoKT) is employed to correct the range curve (RC) induced by the target's acceleration. Thereafter, the centroid axis rotation (CAR) is presented to remove the residual range walk (RW) by rotating the slow time axis parallel to the RW line in the fast-time and slow-time domain. Finally, the phase fractional lower-order Lv's distribution (PFLOLVD) is proposed to compensate the DFM and realize the energy accumulation of the target in the ASN environment. Furthermore, the performance of proposed algorithm in aspects of coherent integration time, computational complexity, and multiple targets detection are analyzed in detail. Compared with the existing coherent integration detection methods, the proposed method is both robust for ASN environments and superior in the detection performance. [ABSTRACT FROM AUTHOR]

End effects of Local Mean Decomposition (LMD) are regarded as a typical problem leading to a distorted decomposed waveform and interfere with the extraction of characteristics. This paper proposes a novel self-adaptive point extension approach based on a Bidirectional Long Short-Term Memory (Bi-LSTM) regression network to eliminate this problem. This approach divides the existing samples into two parts and conducts two training processes, in which the first-training obtains the optimal network initialization parameters and the second-training gets the final extension network to identify the correct extremum. A simulated signal is used to demonstrate the advantages of the proposed approach over BPNN, LSTM, and characteristic segment approaches. The standard LMD method is combined with the proposed extension to form an improved LMD algorithm (ILMD). Finally, ILMD is applied to three experimental vibration signals which are collected from different loading conditions. The results demonstrate that ILMD can accurately extract failure and rotational characteristic frequencies of rolling element bearings with higher amplitude, and accordingly, the error caused by end effects does not influence the extracted information. • A novel point extended approach based on Bi-LSTM regression network. • A self-adaptive trained approach is developed to save insufficient samples. • Three vibrational signals under different loading conditions used to method validate. [ABSTRACT FROM AUTHOR]

Model-based adaptive signal decomposition algorithm is an important research topic in the field of signal analysis. The amplitude modulated and frequency modulated (AM-FM) model is the most common model in the signal. In this paper, an adaptive signal decomposition algorithm based on the complex AM-FM model is proposed. This algorithm first obtains the analytical expression of the analyzed signal; and then the optimal equation of the analytic signal based on the model is realized by initializing the instantaneous frequency (IF) of the model. Finally, the partial differential equation and the alternating direction method of multipliers technique are used to solve the optimization equation to achieve a high precision estimation of the model. Experiments on simulated and real-life data verify that the proposed algorithm can accurately estimate the instantaneous amplitude (IA) and IF of each component in the signal, and has strong robustness and high efficiency. [ABSTRACT FROM AUTHOR]

Recently, the kernel based constrained adaptive filtering algorithm has attracted a lot of attentions because of its robustness and superiority over traditional methods. As two classic kernel based algorithms, both constrained maximum correntropy criterion (CMCC) and constrained minimum error entropy (CMEE) have shown their superiorities in the case of non-Gaussian noise. However, both algorithms use only one kernel as the kernel function. To further improve the performance of the kernel based adaptive filtering algorithms, we first define the mixture kernel risk-sensitive loss (MKRSL) and study its properties. Then, we apply it to the constrained adaptive filtering and propose a novel constrained minimum MKRSL (CMM-KRSL) algorithm in this paper. Furthermore, we present the performance analysis of the CMM-KRSL algorithm, and provide the stability condition and the theoretical mean square deviation (MSD). Finally, we validate the accuracy of performance analysis and the superiorities of CMM-KRSL by simulations. [ABSTRACT FROM AUTHOR]

Compressed Sensing theory breaks through the limitation of the Nyquist sampling law and provides theoretical support for accelerating the imaging process of MRI while reconstructing high-quality images. It can use less sampling data through the reconstructed algorithm to restore the original signal. Reconstruction time and reconstruction algorithms play important roles in compressed sensing. However, it is difficult to apply the iterative reconstruction method in clinical applications because of the long reconstruction time. In addition, hand-crafted image prior which is widely used in traditional iterative method lacks of adaptivity for MRI reconstruction. In this paper, inspired by the total variation model, we propose a novel deep network for CSMRI, dubbed as TV-Inspired Network (TVINet), which incorporates the deep priors into the traditional iterative algorithm. We apply a general compressed sensing reconstruction framework inspired by TV regularization and solve it by combines the iterative method with deep learning. The design of TVINet comes from the inference process on the basis of Primal Dual Hybrid Gradient algorithm, which makes the network has preferable interpretability. The linear operator and regularization term are learned from the training dataset by using a convolution network. The proposed approach trains the network end-to-end and reconstructs the desired image directly from the under-sampling data. Experimental results show that the proposed method has better PSNR or SSIM than state-of-the-art methods, and maintains more complex and fine details in the reconstructed image. [ABSTRACT FROM AUTHOR]

Direction-of-arrival (DOA) estimation is a fundamental problem in many applications of array signal processing. In practice, it is more important to track the DOAs of moving target signals. In this paper, we study the problem of tracking the DOAs of multiple signals in the frequency diversity array (FDA)-MIMO radar system. Aiming at FDA signals, we propose an efficient algorithm for DOA tracking, which combines subspace updating with the DOA estimation of the FDA signal. The proposed algorithm effectively solves the problem of angle-range coupling in the FDA signal. Moreover, it achieves excellent tracking performance and requires low computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

SAR images are a high-resolution map of surface target areas and terrain in the range and the cross-range dimension. Usually, moving targets appear as defocused and spatially displaced objects superimposed on the SAR map. In this paper, a new moving target refocusing imaging method based on Range Doppler (RD) Algorithm and convolutional neural network is proposed. Firstly, Range Doppler (RD) Algorithm is used to preprocess the echo data to obtain the blurring SAR image as the input data of convolution neural network. Secondly, according to SAR imaging characteristics, the original U-net structure is improved to extract the structure information of input data and provide good target reconstruction. The trained network can focus moving targets in case of different azimuth velocities. Finally, the experimental results prove the effectiveness of the proposed method in radar image focusing. [ABSTRACT FROM AUTHOR]

In this paper, a novel and complete derivation of the range doppler algorithm (RDA) formulation is developed for stepped frequency continuous wave (SFCW) synthetic aperture radar (SAR) usage. As the RDA has been originally developed for linear frequency modulated (LFM) pulse excited SAR data, the detailed mathematical derivation of SFCW based RDA formulation is presented with similarities, differences and additions to the traditional LFM-RDA formulation for comparison. Then, the developed SFCW-RDA was tested with simulation data and the performance of the algorithm is determined by examining some important parameters including impulse response width, peak to sidelobe ratio, integrated side lobe ratio, range resolution, and azimuth resolution. The performance metrics of the SFCW-RDA in comparison to LFM-RDA thru the constructed SAR images have proven the implementation validity of the proposed technique. [ABSTRACT FROM AUTHOR]

Poisson noise tends to be the primary source of image degradation in several applications. When reducing the exposure time, the image may be severely degraded by noise. One of the strategies is to use adaptations of Non-Local Means for filtering. The NLM algorithm filters an image using a non-local average weighted by a function of the Euclidean distance between two patches of the image. Since geodesic distances induce a metric for the dissimilarity between two distributions, they can be used to compare two patches of an image. This paper alters the NLM algorithm to filter Poisson noise by changing the distance metric with a geodesic one that is more representative of this type of noise, adopting a general, and computationally efficient Bayesian approach. We evaluate the proposed method in the context of low-dose sinogram denoising. Among the geodesic distances evaluated, we found a closed solution for the Shannon entropy for Gamma distributions. Comparisons are made with other NLM strategies as well as state-of-the-art methods, achieving competitive results. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the performance of various nature-inspired optimization algorithms in the design and analysis of non-uniformly spaced arrays. These algorithms include covariance matrix adaptation evolution strategy (CMA-ES), evolutionary programming (EP), particle swarm optimization (PSO), genetic algorithm (GA), and fireworks algorithm (FWA). First, we compare the performance of these algorithms in optimal placements of elements in non-uniformly spaced linear arrays with uniform weights in order to achieve minimized sidelobe level for a given beam-direction as well as for having nulls at specified angles. We consider linear arrays with both isotropic and finite-length dipole elements, and both without and with mutual coupling effects. We also present the optimization of both spacings and weights in the design of sparse Dolph-Chebyshev arrays in the presence of mutual coupling effects. In addition, we perform the optimization for the case of a two-dimensional planar array. Finally, we apply a variation of the algorithms to perform joint direction-of-arrival and mutual coupling estimation using structured sparse arrays such as nested and minimum redundancy arrays. [ABSTRACT FROM AUTHOR]

In the graph signal processing literature, most methods were developed based on the assumption of Gaussian noise since it can lead to computationally efficient and mathematically tractable solutions. Unfortunately, the Gaussian distribution cannot capture the sharp spikes and tail heaviness of the signal noise in various natural phenomena. The α -stable distribution is a generalization of the Gaussian distribution and is a more appropriate model for such impulsive heavy-tailed noise. In this paper, we consider the problem of adaptive estimation and sparse sampling for signals defined over graphs in the presence of impulsive α -stable noise. To tackle the problems associated with α -stable noise, the graph signal estimation problem is formulated as a minimum dispersion (MD)-based optimization. A novel adaptive least mean p th power (LMP) algorithm is proposed for robust estimation of band-limited graph signals from partial observations in α -stable noise environments. The mean square performance of the proposed LMP algorithm is theoretically analyzed. To handle the case of band-limited graph signals with unknown and time-varying bandwidth and spectral contents, the sparse nature of the Graph Fourier transform of the signal is exploited to develop an adaptive graph sampling technique. Specifically, the proposed sparse sampling technique identifies the spectral signal support via sparse online estimation building on the iterative shrinkage-thresholding algorithm. Numerical simulation studies are presented to corroborate the performance advantages of the proposed adaptive estimation and sparse sampling algorithms. [ABSTRACT FROM AUTHOR]

To locate mixed far-field (FF) and near-field (NF) sources, most existing methods require two or three steps to first find the direction-of-arrival (DOA) and then retrieve the range parameters based on the DOA estimates. Hence, the range estimation performance highly relies on the DOA estimation accuracy in these methods. On the other hand, the MUSIC-like or on-grid sparse methods require discretization of the angle and range spaces which may bring in high computations and grid mismatch problem. In this paper, we propose a new method named Gridless OnE-Step (GOES) to jointly retrieve the DOA and range without requiring the pairing and discretization of the angle and range. In particular, we first construct a virtual single-snapshot signal model by the fourth-order cumulants, and then formulate a semidefinite programming (SDP) based on the atomic norm technique to simultaneously estimate the angle and range information. To reduce the computational complexity, we also propose a projected Wirtinger gradient descent algorithm Wirtinger-GOES (W-GOES) for the implementation. Because of the one-step procedure, our method not only has higher resolution than many existing methods in distinguishing both the angles and the ranges of different sources, but also can locate multiple NF or mixed FF and NF sources closely-located in angle domain or even with the same DOA. Extensive simulations are carried out to verify the superiorities of our method. [ABSTRACT FROM AUTHOR]

In this paper, we consider a three-dimensional parameter space, which is the time-frequency-chirprate (TFCR), to characterize the time-varying features of multi-component non-stationary signals. By performing reassignment on the frequency-chirprate plane, a highly concentrated TFCR representation, named as the frequency-chirprate reassignment method (FCRM), is proposed. FCRM can provide the instantaneous frequency (IF) and chirprate (CR) estimates jointly, making the crossing of IFs appear as separated in the TFCR domain, which overcomes the limitation of the popular time-frequency post-processing methods that require input signal with non-overlapping IFs. Based on the chirplet transform, we derive the reassignment center of the FCRM, and a three-dimension ridge detection algorithm is introduced to extract the IFs and CRs from the FCRM. Numerical experiments demonstrate that the proposed FCRM provides a concentrated TFCR representation, obtaining a good IF estimation for the overlapped multi-component signals that contain cross-over IFs. [ABSTRACT FROM AUTHOR]

Over the last decade remote photoplethysmography (rPPG) algorithms have been developed extensively. As a result, pulse rate can now be accurately estimated by video data for still subjects. However, in realistic conditions both the accuracy of these algorithms and benchmark datasets are far from perfect. In this paper we propose a new rPPG method which enables heart rate detection by video from a standard webcam. The algorithm is robust with respect to such factors as illumination changes or the subject's movements, and can track fast pulse rate changes. To do that, the algorithm determines the approximate value of the pulse rate and then specifies it with high time resolution. In order to comprehensively study the proposed method, we collected a new dataset consisting of videos recorded in various challenging conditions of several categories as well as reference photoplethysmograms recorded synchronously with a contact pulse oximeter. The proposed method showed high performance under all conditions including blinking illumination, speech and large-amplitude movements. We tested two simplified versions of the algorithm, which provided competitive scores as well. However, with human movement videos the full method showed better results than its simplified versions (p<0.001). The proposed algorithm was tested on the existing UBFC-RPPG database and compared with previous methods. Our method showed high results (2.10 MAE, 3.43 RMSE). • A novel robust method for remote photoplethysmography signal processing is proposed. • Pulse rate is approximately estimated and then refined in time and frequency domains. • A new publicly available challenging rPPG dataset called MoLi-ppg is introduced. [ABSTRACT FROM AUTHOR]

Correlation is an index indicating the similarity of different data set, and a correct calculation of correlation or correlation coefficient is very important in science and engineering. However, in many real word applications, the signal will be contaminated by structured impulsive noise, then the generally used correlation or correlation coefficient will fail to correctly express the statistical relationship between two signals. In this paper, in order to reveal the correlation between two matrix signals in the presence of structured or group-wise outliers, we extend the definition of correlation to the ℓ 2 , p -space and define the ℓ 2 , p -correlation and its corresponding correlation coefficient. The properties of the ℓ 2 , p -correlation coefficient are also presented, and an iterative ℓ 2 , p -norm minimization algorithm is proposed to calculate the ℓ 2 , p -correlation accurately. The ℓ 2 , p -correlation is then applied to solve the problem of robust sparse signal recovery, and a greedy pursuit based algorithm is developed to approximate the signal. Furthermore, experiments are carried out on sparse signal recovery and direction of arrival (DOA) estimation to demonstrate the efficiency of the ℓ 2 , p -greedy pursuit approach in sparse approximation in the presence of group-wise outliers comparing to the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) has been widely used in wireless sensor networks (WSNs). In WSNs, the sensors are battery-powered and hence their communication and processing powers are limited. One of the dominant features of the CS is its complex recovery phase. Thus, great care should be taken into account when designing the CS recovery algorithm for WSNs. In this paper, we propose a distributed and cooperative recovery algorithm for two different cooperation modes of sensor networks including incremental and diffusion. The theoretical performance analysis of the proposed algorithms in both exact and noisy measurements is investigated. The obtained results show the superiority of the proposed method in terms of convergence rate and steady-state error compared with the non-cooperative scenario and the well-known distributed least absolute shrinkage and selection operator (D-LASSO) approach. Furthermore, the proposed structure requires much fewer measurements for exact recovery. [ABSTRACT FROM AUTHOR]

This paper derives an identification algorithm for Hammerstein nonlinear systems with dynamic disturbances and measurement noise. The dynamic disturbance is viewed as a time-varying sequence to be estimated and its model structure and excitation signal are not considered. By extending the parameter and information vector, an extended recursive least squares algorithm is proposed first time to identify recursively both the system parameters and dynamic disturbance of a Hammerstein output error model. By constructing matrix forgetting factor, the dot product operation is used to update covariance matrix, which improves the estimation accuracy of time-invariant system parameters and the tracking performance of dynamic disturbance. The auxiliary model technique ensures that consistent estimation of model parameters can be obtained. The adaptive forgetting factor improves the convergence rate of the algorithm under finite sampling data. Numerical example with Monte-Carlo simulation test is used to verify the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel method called fourth-order spatiotemporal algorithm (FOSA) is presented for classification, 2D DOA estimation and 3D localization of mixed near-field and far-field sources using a uniform circular array with a center sensor. By constructing two spatiotemporal cumulant matrices, without range information, and regarding the fourth-order stationary property, the 2D DOA estimation matrix is extracted. By applying the eigenvalue decomposition, the virtual steering matrix is estimated. The sensor in the center of the array is used to extract the valid virtual steering vectors. The 2D DOAs are estimated by a least-squares method and without the use of spectral search. Ranges of near-field sources are estimated by constructing a spatial cumulant matrix and using the 1D MUSIC technique. FOSA provides a reasonable classification of signals types. It can detect near and far field sources possessing the same azimuth or elevation DOAs. Furthermore, it has a much low computational complexity. FOSA prevents array aperture loss without any pairing parameters. The simulation results validate its high performance in terms of estimation accuracy, resolution and the probability of correct classification of the signals types. [ABSTRACT FROM AUTHOR]