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

To mitigate the leakage signal that obstructs the target information extraction in miniatured frequency modulated continuous wave (FMCW) radar system, an adaptive leakage signal cancellation algorithm is proposed in this paper. Without any help of the additional auxiliary path, all the processing for the intermediate frequency (IF) signal can be conducted in the digital domain. The basic principle

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

In large scale information fusion networks the statistical dependencies between nodes are rarely known. Failing to account for this detail in the design of distributed estimation schemes may lead to statistical inconsistencies and may even cause some estimators to diverge at various nodes within the network. Among the approaches that have been proposed to address this problem, covariance intersection

One of the most interesting problems in denoising of images includes despeckling of multiplicative noise. This paper proposes a novel statistical processor in the framework of Non-sub Sampled Shearlet Transform (NSST) to reduce the effect of the multiplicative noise on images given preserving of structural and visual quality of image. First, we indicate that NSST coefficients of logarithmically transformed

Recently, deep-learning-based blind picture quality measurement (BPQM) metrics have gained significant attention. However, training a robust deep BPQM metric remains a difficult and challenging task because of the limited number of subject-rated training samples. State-of-the-art full-reference (FR) picture quality measurement (PQM) metrics are in good agreement with human subjective quality scores

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

Conventionally adaptive detection for frequency diverse array (FDA) radar requires extra training data to estimate the range-dependent interference covariance matrix (ICM). In this paper, we propose an adaptive moving target detection approach for FDA radar without training data, where the ICM is directly estimated by applying a structured generalized likelihood ratio test (SGLRT) to the target-present

With the development of the generative model, image synthesis has become a research hotspot. This paper presents a novel Cross-modal Feature Alignment based Hybrid Attentional Generative Adversarial Networks (CFA-HAGAN) for text-to-image synthesis. It mainly consists of two steps, text-image encoding and text-to-image synthesis. Text-image encoding learns a Cross-modal Feature Alignment Model (CFAM)

An enhanced intelligent fault diagnosis method is proposed based on pairwise graph regularized deep belief network (PG-DBN) model. In this novel framework, two different graph constraints are imposed on hidden layer of the Restricted Boltzmann Machine (RBM). The first graph constraint defines the representation of preserving the feature manifold structure in same class of the data and the second graph

Linear zero forcing (ZF) precoding can obtain near-optimal performance as the number of base station (BS) antennas increases, but involve complex matrix inversion calculations whose computational complexity is cubic with respect to the number of user equips (UEs) in massive multiple-input multiple-output (MIMO) systems. In this paper, a low-complexity modified successive over-relaxation-based ZF (MSOR-ZF)

A categorical study on iris localization, its challenges and techniques is carried out here. This survey comprehends the historical evolution and present state-of-the-art in iris localization. Most research publications include their primary contribution in iris localization based on tools like Hough Transform, Integro-differential Operator, Active Contour and Deep Learning etc. Majority of the techniques

This article presents new expressions for the bit error probability (BEP) of the M-ary quadrature amplitude modulation (M-QAM) and rectangular QAM (R-QAM) scheme in a frequency non-selective channel model with cascaded double fading, characterized in the links by the distributions η-μ, κ-μ or α-μ. In this paper, the probability density function (PDF) of the signal-to-noise ratio (SNR) of the double

Signal parameter estimation like direction of arrival (DOA), polarization and Doppler frequency for conformal arrays with a reasonable computational effort is cumbersome, because conformal arrays have complex steering vectors without Vandermonde structure but involves distinct antenna patterns. To this end, we propose a fast estimation algorithm which enjoys computational benefit stemming from processing

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;

Due to their nice properties, unimodular sequences possessing low autocorrelation sidelobes have already been employed as transmit waveforms in numerous radar systems. It could also be desired to have a transmit waveform which avoids certain frequency bands allocated for specific communication applications. Hence, adaptation and specific design of the waveform spectrum might also be essential. Accordingly

The detrended fluctuation analysis (DFA) and its variants such as the detrended moving average (DMA) are widely used to estimate the Hurst exponent. These methods are very popular as they do not require advanced skills in the field of signal processing and statistics while providing accurate results. As a consequence, a great deal of interest has been paid to compare them and to better understand their

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

The anisotropic total variation (TV) denoising model suppresses noise for two-dimensional signals that are vertically and horizontally piecewise constant. However, two-dimensional signals may have sparse derivatives in other directions. We propose a modification of the classical anisotropic two-dimensional TV regularizer from a spectral point of view. In the frequency domain, the TV regularizer can

In machine-type communications (MTC), a large number of devices are connected to an access point (AP), but only a few devices are active at a time. This sparse activity of devices makes compressed sensing (CS) technique a possible solution for joint activity detection and channel estimation problem in MTC. In this paper, we improve the performance of greedy CS based detection in MTC, by exploiting

Orthogonal frequency division multiplexing (OFDM) systems based on discrete fractional Fourier transform (DFRFT) chirp basis replacing the complex exponential basis of discrete Fourier transform (DFT) is extensively studied in the literature. Authors of this paper have recently proposed fractional Fourier domain equalization (FFDE) for an OFDM system with a discrete cosine transform (DCT) multicarrier

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

In practical application, the time when the maneuvering targets enter and leave the radar coverage observation is usually unknown, which will seriously deteriorate the performance of coherent integration and target detection. To solve this problem, we propose a method based on the symmetric autocorrelation function and the proposed window Lv's transform (SAF-WLVT) for coherent integration and motion

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

3D-high efficiency video coding (3D-HEVC) is the latest extension of HEVC standard. Through adopting novel coding tools, 3D-HEVC could improve the compression efficiency of multi-view texture video. However, with the great bitrate savings, 3D-HEVC brings the extreme complexity. To reduce the encoding time of texture video, a fast 3D-HEVC method based on just noticeable difference (JND) model is proposed

In this work, we address the problem of tracking multiple acoustic sources by using an array of microphones in reverberant and noisy environments. Generally, the time difference of arrival (TDOA) measurements are obtained by the peak extraction of the Generalized Cross Correlation (GCC) function, and then used to update source states in the framework of target tracking. In adverse environments, due

The noise generated by some rotating machines usually presents a mixture of tonal and chaotic signals. The hybrid functional link artificial neural network (HFLANN) consisting of sinusoidal noise canceller (SNC), narrowband ANC (NANC) and FLANN subsystems was a great success for this kind of noise. However, it has no ability to overcome nonlinear acoustic path and frequency mismatch, and its convergence

We present a deep network-based blind image quality assessment (BIQA) using two-side pseudo reference (TSPR) images. Different from a traditional reference with the perfect quality, two opposite images are introduced as pseudo references, which are generated via a restoration model and a degradation model, respectively. The bilateral distance (error) maps between the TSPR images and the pristine distorted

Distributed multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) configuration, which is composed of multiple distributed dual-function transmitters, multiple radar receivers and multiple communication receivers, are capable of performing target state estimation and information transferring tasks simultaneously. In this article, the authors put forward a low probability of

In the direction estimation of noncircular signals, the sensor array can be virtually extended by adding its complex conjugated version. Exploiting the centro-Hermitian of the extended signal vector without specific requirements of array geometry, we present a computationally efficient method for direction estimation based on the multiple signal classification (MUSIC) principle. When the MUSIC-like

This paper investigates the problem of separating galaxy spectra resulting from the slitless spectroscopy system, which can e.g. be used in spatial missions like the WISP survey and the Euclid project. We first derive a physical mixing model linking observed data to source spectra, then simplify it to obtain two different approximate but realistic models. The first simplified model being entirely defined

Graph signal processing deals with signals which are observed on an irregular graph domain. While many approaches have been developed in classical graph theory to cluster vertices and segment large graphs in a signal independent way, signal localization based approaches to the analysis of data on graph represent a new research direction which is also a key to big data analytics on graphs. To this end

The multiple signal classification (MUSIC) algorithm is widely used in direction of arrival (DOA) estimation. Conventional MUSIC-like algorithms suffer from the heavy computational burden imposed by the two-dimensional (2-D) angle search and an exhaustive spectral search. We propose a two-stage unitary spherical harmonics MUSIC (TSU-SHMUSIC) that converts 2-D MUSIC into two new one-dimensional (1-D)

Target detection is a challenging problem in the complex detection environment of high-frequency surface wave radar (HFSWR). Due to the existence of various clutter and interferences, the detection environment in HFSWR is highly non-homogeneous with sharp/smooth clutter edges and multiple interfering targets. In this paper, we propose an effective detector for the non-homogeneous Weibull clutter in

Currently, sparse Bayesian learning (SBL) has been introduced to solve direction of arrival (DOA) estimation in different situations. In the line of DOA estimation under impulsive noise, existing SBL-based methods need large computation which will restrict their practicabilities. To address this problem, we propose an efficient method based on a real-valued SBL for non-circular signals in this paper

Sparse Blind Source Separation (sBSS) plays a key role in scientific domains as different as biomedical imaging, remote sensing or astrophysics. Such fields however require the development of increasingly faster and scalable BSS methods without sacrificing the separation performances. To that end, we introduce in this work a new distributed sparse BSS algorithm based on a mini-batch extension of the

The Fourier representations (FRs) are indispensable mathematical formulations for modeling and analysis of physical phenomena and engineering systems. This study presents a new set of generalized Fourier representations (GFRs) and phase transforms (PTs). The PTs are special cases of the GFRs and true generalizations of the Hilbert transforms. In particular, the Fourier transform based kernel of the

Graph matching is an important problem in computer vision and pattern recognition. This paper proposes a graph matching algorithm based on a directed graph structure model. Firstly, the point set is divided into two parts by using the line between two points, and the direction information between the two points is specified according to the number of points on both sides of the line. Secondly, the

In recent years, distributed beamforming has attracted a lot of attention. Since each node has its own processing power, one significant advantage is the capability of distributed computing. In general, almost all distributed beamforming approaches are solving certain multi-block optimization problems. However, additional conditions are usually required to ensure convergence. In this paper, a new distributed

Mainlobe jamming will reduce the capabilities of modern radar significantly. Conventional independent component analysis (ICA)-based methods will suffer from the ineffectiveness when the angle of the target is same as that of the jammer. In this paper, exploring polarization characteristics, we propose an approach based on ICA for polarimetric multi-channel radar to suppress mainlobe interference.

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

This paper focuses on the error probability performance of LoRa, a long-range low-power wireless communication technology suited for the Internet of Things (IoT). We propose accurate approximations for the bit error probability of the uncoded LoRa chirp modulation in additive white Gaussian noise (AWGN) channel. Our approach, which is based on a smart modification of a union bound on the error probability

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

Conventional distributed collaborative beamforming would generally wrestle with the challenges of slowed-down convergence rate, prohibitively high computational costs and inefficient communication when applied to large-scale networks equipped with massive arrays. To address these challenges, we herein reformulate the distributed collaborative beamforming problem from the tensor perspective. By exploiting

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

This work studies the near-field localization problem using a symmetry uniform linear array (ULA). To decouple the range and direction of arrival (DOA), by exploiting the symmetry property of the array, two spatial correlation sequences are constructed, where each sequence only corresponds to one parameter, i.e., DOA or range. After decoupling, an attractive property is that the resulting sequences

Mixture models are broadly applied in image processing domains with hierarchical Bayesian approaches popularly considered for grouped data. The related inference process takes into consideration both the approximation of exact data shapes and estimation of adequate component numbers. In our work, we develop nonparametric hierarchical Bayesian models using the Dirichlet and Pitman-Yor processes with

Downlink multiuser network coding (NC) automatic repeat request (ARQ) systems can provide significant throughput improvements over basic ARQ systems. In this paper, we consider the case of two-unicast flows and analyze their performance in terms of the effective capacity (EC) under retransmission schemes. We extend the derived EC of NC-ARQ for asymmetric channels where users have different data rates

In most traditional multi-focus image fusion algorithms, the focus measures used to detect the focused areas of multiple images are unstable and sensitive to noise. Although many modified methods implement more sophisticated strategies to cope with this problem, the complexity of these methods turns out to be a problem for mobile devices. In order to cope with these problems, a novel multi-focus image

Existing stereoscopic 3D (S3D) salient object detection (SOD) networks typically employ a two-branch architecture, in which the RGB and depth channels are learned independently. Conventional methods based on conventional neural networks generally fuse the two branches by combining their deep representations at a later stage with only one path, which can be inefficient and insufficient for retaining

Object proposal quality assessment without ground truth as reference is a challenging task. Some existing methods measure the quality with hand-crafted metrics for subjective metrics, such as objectness and foreground confidence. Recently, deep learning is adopted for direct assessment for quantifiable metric, such as Intersection over Union (IoU). However, we find that IoU, the commonly used quality

This paper presents analytical superdirectivity solutions for circular arrays with acoustic particle velocity sensors (APVSs) based on the eigenbeam decomposition and synthesis theory. The main findings are summarized as follows. (1) The optimal beampattern, the global directivity factor, and the total error sensitivity function of circular APVS arrays, like those of circular arrays with pressure sensors

This work deals with the problem of fast direction-of-arrival (DOA) estimation. A low complexity iterative off-grid method is proposed, which employs the approximate message passing with unitary transformation based sparse Bayesian learning (SBL) to obtain initial estimates of the signals and their corresponding DOAs, and then refines the estimates iteratively using the Jacobi or Gauss-Seidel iteration

In a low angle scenario, the radar performance is significantly degraded because of the multipath. Time reversal is an efficient technique to improve the target detectability by focusing the energy from different paths. For multiple targets and rich multipath scattering case, the number of echoes is large and the distance estimation is complex. In this paper, a distance estimation method of multiple

In this paper, a robust watermarking algorithm in integer wavelet domain using downsampling is proposed. The innovations of this paper can be summarized as follows. First, after downsampling both the host image and watermark, each sub-watermark is embedded into the corresponding sub-host image. Second, four sub-watermarks are encrypted by pixel scrambling and fractional random transform, and the right

Since the introduction of finite rate of innovation (FRI) signals in 2002, various deterministic and stochastic techniques have been proposed to estimate the innovative parameters of FRI signal, e.g. time instants tk and weights ck belonging to a linear combination of finite number of Diracs, from its noisy samples regardless of sampling kernel's type. Having analyzed the Bayesian methods dedicated