The Stockwell transform (ST), which is an invertible time-frequency spectral localization technique, uniquely combines elements of wavelet transforms and short-time Fourier transforms. However, its signal analysis ability is restricted in the time-frequency plane. In this paper, the novel fractional Stockwell transform (FRST) is proposed to address this problem, based on the ST and fractional Fourier

In modern radar detection systems, constant false alarm rate (CFAR) control is a key technique for automatic target detection in clutter level unknown environments. The maximum likelihood criterion is often used to design the CFAR detector for which it has the best detection performance with the expected probability of false alarm. However, the heterogeneous environments, viz., multiple-target scenario

This paper investigates a countermeasure model between a multistatic multiple-input multiple-output (MIMO) radar network and multiple targets in the presence of continuous surface clutter. As a member of the multistatic MIMO radar network, the main purpose of each radar is to minimize its transmit power under a certain target detection criterion. Based on the selfishness of each radar, a strategic

Blind identification of channel codes is becoming increasingly important in signal interception and intelligent communication systems. However, most existing channel codes recognition algorithms extract features manually, which makes them highly demanding in real-world application. Thus, efficiently identifying channel codes is difficult using present technologies. This paper presents a deep residual

Despite the advantage of power efficiency and spectral efficiency, spatial modulation (SM) technology makes communication systems sensitive to the transmission impairment. This paper analytically investigates the performance of SM multiple-input multiple-output (MIMO) systems where power amplifier (PA) shows clear nonlinearity. Firstly, the system model is presented with nonlinear distortion included

The compressive sensing theory enables reconstruction of sparse or compressible signals at reduced sampling rate. Recent studies have shown that stable signal reconstruction is possible even if each measurement is quantized to one bit. In conventional compressive sensing framework, the signal can be sparsely represented by some discrete atoms. In many applications however, signals are sparse in a continuous

This study proposes a novel blind watermarking algorithm based on radial harmonic Fourier moments (RHFMs) as well as discrete wavelet transform and discrete cosine transform (DWT-DCT) to achieve copyright protection. A non-adjacent coupled map lattice chaotic system was used to improve the safety in the watermark embedding process. First, a zero-watermark was obtained by performing DCT on the original

Face image recognition technology plays an important role in biometric recognition field. Among the present face recognition methods, the method based on subspace learning have aroused wide concern due to their favorable properties, such as convenience for computation and effectiveness for identification. However, existing methods based on subspace learning are out of work when the sample-specific

In this paper, a new transmitter-receiver structure for high speed train (HST) data transmission and detection in fifth generation (5G) communication systems is proposed. This structure employs a new Doppler shift frequency (DSF) estimation approach, projected in this paper, to calculate the channel taps' DSFs at the base station (BS) receiver. The estimated DSFs are used to calculate the channel taps'

Orthogonal time frequency space (OTFS) is a recently introduced modulation technique that multiplexes information symbols in the delay-Doppler (DD) domain rather than the time frequency (TF) domain as in traditional multi-carrier modulation such as orthogonal frequency division multiplexing (OFDM). It has been shown that OTFS outperforms OFDM in a high Doppler environment. OTFS can be utilized in the

The particle filter (PF) has excellent estimation performance for nonlinear non-Gaussian systems. However, this method misleads the results due to sample impoverishment and the sensitivity on the initial values of state and process noise variance. To overcome this, an intelligent PF method with a resampling of multi-population cooperation is presented in this paper. Firstly, an intelligent resampling

The complex least mean square (CLMS) adaptive algorithm based on the minimum mean square error (MSE) criterion has been widely used for linear applications with Gaussian noises in complex domain. However, the MSE criterion suffers from performance degeneration in the presence of non-Gaussian noise. To address this issue, a novel complex kernel risk-sensitive p-power loss (CKRSP-L) criterion is first

Radar detection in environments where the power and variability of interference hinders the location of targets is of great importance. Many efforts are directed towards the development of adaptive techniques that process the eco-signals in order to increase the detection quality and to maintain a constant false alarm rate (CFAR). In this context was conceived the radar detection in the moments space

Binary pattern family is considered as a powerful tool for visual texture classification. Most popular methods improve the classification performance by multi-feature fusion. However, many sub-features are redundant and low-discriminative and the classification system has high computational complexity and unsatisfactory results. To handle above problems, this paper proposes a compact multi-pattern

In this paper, we propose a novel method for dual-function radar-communication(DFRC) system. Our focus is on radar being used for communications in parallel to conventional target tracking and/or surveillance assignment. In the proposed setup, the main beam is directed toward the targets as primary radar function, whereas, communication is performed through the sidelobes as secondary function. In addition

Track-before-detect (TBD) of weak targets with motion model uncertainty is of significance in practical applications, since targets may experience various motions. In this paper, a unified TBD framework is presented for multiframe detection of weak targets with constant velocity (CV) and coordinated turn (CT) motions. This enables detection and parameter estimation of weak CV and CT targets in a same

An accurate estimation of the noise power from noisy data leads to better estimation of signal-to-noise ratio (SNR) and is useful in detection, estimation, and prediction. The major contributions of this paper are to estimate the polynomial degree and the noise power from data coming from an underlying polynomial with additive Gaussian noise, using an AR model. The two proposed methods have been inspired

Coprime arrays enable Direction-of-Arrival (DoA) estimation of an increased number of sources. To that end, the receiver estimates the autocorrelation matrix of a larger virtual uniform linear array (coarray), by applying selection or averaging to the physical array's autocorrelation estimates, followed by spatial-smoothing. Both selection and averaging have been designed under no optimality criterion

This paper describes a robust sparse Bayesian learning approach for DOA estimation in the presence of impulsive noise. In practical situations, impulses in the noise often appear in bursts, resulting in structured rather than independent sparsity, which can be exploited to enhance DOA estimation performance. However, there is no method available for capturing the realistic burst structure of impulsive

In this paper, the problem of low angle estimation for multiple-input multiple-output (MIMO) radar is considered. Though the refined maximum likelihood (RML) algorithm can be directly applied to low angle estimation in MIMO radar, its computational burden is huge due to the large virtual aperture. Moreover, the conventional MIMO radar employs full waveform diversity, which actually sacrifices the emission

The interference from clutter and active electromagnetic equipment deteriorates target detection performance greatly. In this paper, we design a MIMO radar transmit matrix to improve target detection performance in the presence of interference. To this end, we model the design problem by maximizing the signal to interference ratio (SIR) with the constraints of spectrum compatibility and constant modulus

Probabilistic linear discriminant analysis (PLDA) has achieved good performance in face recognition and speaker recognition. However, the computation of PLDA using the original formulation is inefficient when there are many training data, especially when the dimensionality of the data is high. Faced with this inefficiency issue, we propose scalable formulations for PLDA. The computation of PLDA using

Image segmentation is an essential analysis tool in the field of computer vision, and the level set method has been widely used in image segmentation. Specifically, the edge-based level set models can reduce many undesired regions because they mainly rely on the edge information. However, the edge-based level set models are usually sensitive to the initial condition, which limits their application

This paper proposes a dimensionality reduction of interference subspace to improve the interference suppression capability of null-space expansion (NSE) for multiuser massive multiple-input-multiple-output (MIMO) systems. The fundamental concept of the NSE is to nullify the interference subspace generated by the combined multiple past channel state information (CSI) vectors. It can suppress inter-user

The fault diagnosis of the common rail injector is an important means to ensure the safe operation of the diesel engine. In order to quickly and accurately identify the fault status of common rail injectors, this paper proposes an intelligent fault diagnosis method for common rail injectors based on Composite Hierarchical Dispersion Entropy (CHDE) and Improved Grasshopper Optimization Algorithm based

Human pose estimation is an important problem which has found applications in different fields. One problem in this context is to estimate the human joints or keypoints under occlusion. Most of the methods in the literature use CNN structures to estimate keypoint positions. In this paper, a Bayesian Approach for occluded Keypoint Estimation, BAKE, is presented to complete the missing human pose elements

Insights about the dominant dynamics, coupled structures and the unknown uncertainties of the pandemic diseases play an important role in determining the future characteristics of the pandemic diseases. To enhance the prediction capabilities of the models, properties of the unknown uncertainties in the pandemic disease, which can be utterly random, or function of the system dynamics, or it can be correlated

Existing studies have shown that the problem of blind source separation of quasi-stationary sources (BSS-QSS) with local dominance property can be solved with the convex geometry method. However, it is also illustrated that its performance can seriously degrade if sources are correlated. To address the source cross-correlation problem, in this paper, an effective pre-processing approach is proposed

The joint use of the OFDM waveform for radar as well as for communications has gained high attention recently. One problem that arises from using this waveform is the high spur level that appears on the range-velocity profile where it may eclipse an existing target of relatively small radar cross-section (RCS). This spur is due to nulling some subcarriers such as the DC-subcarrier. Another problem

Drift or error accumulation is an inevitable challenge in visual odometry (VO). To alleviate this issue, most of learning-based VO methods focus on various long and short term sequential learning schemes, while lose sight of the fact that inaccurate rotation estimate is the main source of VO drift. They usually estimate the six degrees of freedom (DoFs) of the camera motion simultaneously, without

In this paper two procedures are proposed for the estimation of the frequency, amplitude, and phase of sine-waves affected by either wideband noise or by both noise and harmonic distortion, respectively. The procedures are very simple and suitable for real-time applications. According to them, firstly the signal parameters are estimated by means of the new Corrected Interpolated Discrete Fourier transform

Early fault diagnosis of common rail injectors is essential to reduce diesel engine testing and maintenance costs. Therefore, this paper proposes a new common rail injector early fault diagnosis method, which combines the Maximum Second-order Cyclostationary Blind Deconvolution (CYCBD) optimized by the Seagull Optimization Algorithm (SOA) and Hierarchical Fluctuation Dispersion Entropy (HFDE). First

Current sensor technologies enable the passive and continuous monitoring of human behaviors as well as infrastructures to ensure personal safety and assess individual health state. One passive technology that has the potential of gathering personal data is the vibration sensor. In this paper, we carry out an extensive survey of the current vibration-based sensing technologies for human and infrastructure

Sampling theorems for the two-dimensional linear canonical transform (LCT) in polar coordinates deserve special attention in medical imaging such as computerized tomography and magnetic resonance imaging due to the superiority of solving the traditional non-bandlimited images processing problems. Two types of interpolation formulae that interpolate in both radius and azimuth the angularly periodic

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

The paper deals with designing a mask suitable for a selective filtering of data. The design of the mask is performed in the time-frequency domain and the selection is based on the co-movement measure of time series. We propose two approaches for the mask construction: i) hard thresholding based on χ2 testing; ii) adaptive based thresholding. The proposed mask can be used for time series filtering

To make the sensor array beamformer insensitive to steering vector error, an efficient interference steering matrix (ISM) estimation scheme for weight vector correction is devised in this paper. The basic idea of this approach is converting the training data into interference-free condition via ISM estimation, thereby correcting the weight vector by the eigen-projection technique. In particular, the

Although ensemble empirical mode decomposition (EEMD) can suppress the modal confusion phenomenon in the EMD method to a certain extent, the added white noise cannot be completely neutralized. The complementary EEMD (CEEMD) adds white noise with opposite signs to the analysis signal in pairs, which greatly reduces the reconstruction error. Aiming at the problems of modal confusion and the difficulty

Recently, near-field 3-D synthetic aperture radar (SAR) imaging has become a research focus in the SAR field. The target extraction is an important step in 3-D SAR image application. As far as we know, there are few studies on the target extraction for 3-D SAR images. One of the difficulties that the target extraction has to face is the influence of the environmental interference in the background

Multiple-snapshot maximum-likelihood (ML) direction of arrival (DOA) estimation problem is studied for the intermittent jamming scenario. The intermittent jamming modality is based on the assumption that only a subset of the collected snapshots are contaminated by the jammer while the others are jammer-free; but the receiver does not know which is which. This type of jamming is frequently encountered

Detecting an accurate foreground from a video frame is a critical task under different complex situations such as sudden changes in illuminations, relocation of background objects, shadow, low contrast videos and dynamic backgrounds (like waving tree, rippling of water etc.). Most of the existing schemes utilize a single feature-based foreground detection approach, which in turn is hard to apply under

In recent years, the EMD-based steganographic method has become more and more popular because of its easy implementation and high security. However, it had two shortcomings: One is the secret data must convert into non-binary numeral system, and the other is low payload. In order to overcome these shortcomings, some studies used the hexagon (or octagon)-shaped reference matrix instead of the extraction

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

Underdetermined blind source separation (UBSS) objective is to recover the source signals from a number of mixtures without any information about the mixing system. Technologies such as teleconferencing, hearing aids and hands-free telephony require real time processing, as long delays are considered intolerable in interactive two-way communication. This is a major challenge in BSS, as algorithms generally

Spatial time-frequency distribution (STFD) utilizes the spatial time-frequency characteristics of chirp signals and effectively improves the direction of arrival (DOA) estimation performance. However, the existing methods based on the STFD matrix assumed that each source has good time-frequency point selection performance. In practice, the time-frequency point selection accuracy of chirp signals suffers

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

In the past decades, image denoising has been widely studied as a basic technology for image processing. However, most denoising methods are designed for Gaussian noise, while few researches focus on the suppression of mixed Poisson-Gaussian noise, which usually found in infrared images. In order to remove the mixed noise in infrared images, this paper proposes a denoising method based on the variance-stabilizing

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 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

Spatially varying motion deblurring has recently witnessed substantial progress due to the development of deep neural network. However, most existing CNN-based methods involve two major shortcomings: (1) The CNN weights are space-sharing, and these methods thus ignore the properties of complex spatially variant blurs which vary from pixel to pixel in natural blurry images. (2) Stacked convolution layers

Deep reinforcement learning has recorded remarkable performance in diverse application areas of artificial intelligence: pattern recognition, robotics, object segmentation, recommendation-system, and gaming. In recent times, the applicability of deep learning to telecommunication technology is gradually attracting a lot of attention, especially in spectrum sensing, a core component in cognitive radio

Three main requirements of a successful application of deep learning are the network architecture, a large enough training dataset, and a good optimization algorithm. In this paper we mainly focus on the optimization part. We propose a training algorithm for convolutional neural networks which makes use of both first and second order derivatives for training different layers. We utilize an approximate

The matrix completion problem can be found in many applications such as classification, image inpainting and collaborative filtering. In recent years, the emerging field of graph signal processing (GSP) has shed new light on this problem, deriving the graph signal matrix completion problem which incorporates the correlation of data elements. The nuclear-norm based methods possess satisfactory recovery

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

A novel direct passive localization technique with a single moving array is proposed in this paper. By using the sparse representation of the array covariance matrix in spatial domain, the measurement is constructed by stacking the vectorized version of all the array covariance matrices at different observing positions, and the dictionary is composed of the steering vectors from the searching targets'

Multiple clusters of particles characterized in terms of fixed-position and fixed-distribution are utilized for target capture, target state estimation, and track maintenance in traditional particle filters. It is often unsustainable in target acquisition and track maintenance since the prior information of target is hard to acquire. In addition, the obtained measurement information may be fuzzy (interval

Global Navigation Satellite Systems (GNSS) have been routinely used for geodetic-based survey and mapping studies such as precise point positioning, landslide, earthquake and crustal deformation monitoring, engineering surveys, in short, where accurate positioning is required. To do that, the GNSS observables should be eliminated from the error sources. Among the error sources affected to GNSS data

Smart jammer and smart anti-jammer have always been attacked and defensed in a contradictory way. In fact, there exists fundamental trade-off between all evolved parties. It is well known that only through interactive training with powerful opponents can the strategy optimization ability in actual combat be improved. In the process of electronic countermeasures between the communication system and