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

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

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

This paper considers the problem of robust angle of arrival (AOA) source localization in the presence of outliers by using sparsity regularization. Firstly, the adaptive regularization (AR) and group-based regularization (GR) are respectively developed based on the cluster information of intersections of pairwise bearing lines to handle outliers. However, the estimated source position based on pseudolinear

In this paper, we propose a mutual interference alignment (IA) method for interference elimination in co-existing multiple-input multiple-output (MIMO) radar and multi-user MIMO communication systems, namely, radar-communication systems. Traditional IA methods proposed for communication systems cannot be directly adopted in the radar-communications due to failure to fulfill the requirements of radar

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

As a design goal of green wireless communication, it is an important consideration to maximize energy efficiency of a Massive multiple-input multiple-output (MIMO) downlink system. By considering the case where the base station and all the users have the imperfect channel state information, a power allocation optimization problem is formulated to achieve the maximization of energy efficiency. Also

Reasonable pricing of options in the financial derivatives market is crucial. For American options, or when volatility and interest rate are not constant, it is often difficult to obtain analytical solutions to the Black-Scholes (BS) equation. In this paper, the Laguerre neural network was proposed as a novel numerical algorithm with three layers of neurons for solving BS equations. The validity period

A novel blind source separation (BSS) method for time-delayed mixtures in underdetermined case is studied in this paper. The proposed method not only addresses the problem of source separation with limited sensors but also avoids the influence of propagation delay. Firstly, the sparse domain is converted by utilizing the spectrum of observed signals to perform modulus operation in time-frequency (TF)

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

This paper presents a multisegment optimization design algorithm for both even- and odd-order variable fractional-delay filters. The proposed algorithm is based on segmenting the fractional delay and separately solving the filter coefficients in each segment. Moreover, the lower bound of the polynomial order and the upper bound of the subfilter design error are derived. These bounds enable us to prove

In this study, the transmissibility estimation of novel coronavirus (COVID-19) has been presented using the generalized fractional-order calculus (FOC) based extended Kalman filter (EKF) and wavelet transform (WT) methods. Initially, the state-space representation for the bats-hosts-reservoir-people (BHRP) model is obtained using a set of fractional order differential equations for the susceptible

Detection of unmanned aerial vehicle (UAV) is a challenging problem for passive bistatic radar (PBR) because of its low radar cross-section (RCS). Range migration (RM) will occur within one long coherent processing interval, which makes it difficult to increase coherent integration gain and improve radar detection ability. In order to eliminate the RM, a novel coherent integration method based on stretch

We model the Haplotype Assembly Problem (HAP) as a minimization problem over an (n−1)-dimensional sphere, where n is the haplotype length. A manifold optimization approach is proposed to solve this problem. To escape the saddle points, the Riemannian trust region method is utilized and its convergence is proved. Simulation results over both real and synthetic data show that the proposed method is considerably

Direction-of-arrival (DOA) estimation performance degrades when arrival angles of incident signals do not locate exactly on the discretized grid points. Existing off-grid DOA estimation algorithms either suffer from high computational complexity or are restricted to uniform linear arrays (ULAs). In order to overcome these disadvantages, we propose a simple but effective off-grid DOA estimation method

In this paper, novel techniques of eigenvalue-based cooperative spectrum sensing (CSS) using Kernel fuzzy c-means (KFCM) clustering are proposed. Test vectors derived from measured eigenvalues are categorized into channel available and unavailable class by performing clustering in two/three dimensional space. This is in contrast to existing eigenvalue-based spectrum sensing techniques, where sensing

The spectral efficiency (SE) of an uplink cell-free large-scale multi-input multi-output (MIMO) non-orthogonal multiple access (NOMA) system which relies on the superimposed pilot (SP) transmission strategy is investigated. Closed-form expressions of the achievable SE with the maximal ratio combining (MRC) receiver filters are derived and compared against the orthogonal multiple access (OMA) counterparts

The problem of recovery of missed values for discrete time processes is considered in a pathwise setting without probabilistic assumptions. A new class of transfer functions is proposed for recovery of finite sets of missed values. It is shown that error-free and uniform recoverability is feasible for classes of square-summable sequences with Z-transform vanishing with a mild rate at periodically located

Data collection is a basic application of wireless sensor networks (WSNs). In practice, only a subset of sensor nodes is selected for data sensing and transmission due to the bandwidth constraint of the channel, energy constraint of the nodes, or malfunctions of the nodes. Data recovery from incomplete sensing data is vital to WSNs. Many works perform data recovery by utilizing the low-rank property

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

Blind multicarrier waveform recognition has become a more daunting task and open problem for the current and future radio surveillance and signals interception, with the advent of new multicarrier technologies such as the state-of-the-art F-OFDM, UFMC, FBMC, OTFS, GFDM and CP-OFDM techniques. Therefore, the practical recognition scheme for multicarrier waveforms is necessary to keep up with the pace

We propose an anomaly detection algorithm based on nonparametric Bayesian (NB) background estimation for hyperspectral images. The background is modeled as a Gaussian mixture model (GMM) and the model parameters and order are estimated from the data in an unsupervised manner using nonparametric Bayesian methods called Chinese restaurant process mixtures (CRPM) and truncated Dirichlet process mixtures

In this article, we propose a new variational model for segmenting images with intensity inhomogeneity. The proposed model applies simultaneously the local constant and global smoothness priors to describe the bias part such that our model can obtain more precise segmentation results. This is different from the existing models in which either of such two priors is considered. Also, our method is developed

Dictionaries with separable structure reduce the computational load of sparse coding and learning algorithms and ensure that patterns present in 2D data are not broken by vectorization. We propose an adaptation of the sparse Bayesian learning (SBL) framework for sparse approximation to the 2D separable case. Our algorithm has two stages. In the first, the hierarchical prior model targets the sparsity

In this paper, a direction of arrival (DOA) estimation algorithm for coherent distributed (CD) sources considering unknown mutual coupling in massive MIMO systems is proposed. By utilizing an enhanced sample covariance matrix (SCM) that matches well with massive MIMO systems, the proposed algorithm constructs a perturbed model and further exploits the sparse total east squares (STLS) technique to achieve

Tracking performance significantly relies on the quality of filter initialization, including accuracy and consistency, especially in a nonlinear filtering system. Most existing methods are built on assumptions that the target position varies linearly with time or the dynamic model is linear. In practical applications where the assumptions are violated, these methods may suffer from performance degradation

An error correcting code type recognition technique based on a deep learning approach is proposed in this paper. This problem could be addressed in the context of non-cooperative communications or adaptive coding and modulation. Inspired by text classification, we proposed a convolutional neural network (CNN) model improved by embedding and block mechanism to classify the linear block code, convolutional

Images captured in rainy days are often obscured by rain streaks which affect the accuracy of object detection, vehicle and pedestrian recognition. It is hard to restore the texture and color information of the de-rained image by some conventional rain removal algorithms. In order to address the problem, we propose a novel two stage conditional generative adversarial network (TS-CGAN), in which the

Recent developments in convolutional neural networks (CNNs) have significantly improved the results of salient object detection (SOD), particularly RGB-D SOD. This article proposes BPA-Net (Boundary-aware Pyramid Attention Network), a network that addresses two key issues in RGB-D SOD based on CNNs: 1) accurately locking the position of an object when it is unclear whether it is a multi-object or a

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

In this paper, we study the tilt optimization method in a single-cell multi-user massive multiple-input multiple-output (MIMO) system. We use the asymptotic channel orthogonality to derive the asymptotic sum rates in the uplink and the downlink, respectively. Based on the correlation property of the array steering vectors, the users are grouped based on the elevation direction-of-arrivals. Then, the

Interspersed duplicated insertion (idINS) is a common type of genomic insertion and plays an important role in genomic instability in cancer genesis. Nevertheless, the detection of such type of insertions is challenging, since the reads originated from idINS regions in the donor sample are most likely to be mapped perfectly to other regions in the reference. Most of the existing approaches adopt paired-end

This paper provides a direction-of-arrival (DOA) estimation method based on sparse Bayesian learning for a colored noise environment. In this method, the harmonic noise model is absorbed into the covariance matrix model to express the noise objectively. As such, the covariance matrix is parameterized with the signal powers and noise parameters. Given that the existing Bayesian models cannot be directly

This paper investigates estimation of the secondary path (SP) during the online operation of the filtered-x least mean square (FxLMS) algorithm-based feedback active noise control (FBANC) systems. The proposed method develops upon a previous work where two adaptive filters were used, one for active noise control (ANC) and the other for secondary path modeling (SPM). The proposed method essentially

Kernel method is an effective way to solve the problem of nonlinear mode analysis, and its key is the selection or construction of kernel function. This paper firstly induced entropy-like divergence by combining Jensen-Shannon/Bregman divergence with convex function, its mercer kernel function called entropy-like divergence kernel is also constructed. Secondly, an adaptive noise distance based on entropy-like

This article tackles the topic of performance analysis for Spectrum Sensing based on Compressive Sampling (CS). More precisely, the lower bound on the variance of any unbiased estimator, the Cramér-Rao Bound (CRB), is investigated in the context of spectral parametric estimation. Compressed samples are obtained from a multiband architecture like the Modulated Wideband Converter, the Quadrature-Analog-to-Information

This paper studies event-triggered sequential fusion filtering problems for multi-sensor systems where observation noises are mutually correlated at the same moment and correlated with the process noise at the previous moment. To save energy consumption of sensors, an event-triggered mechanism is employed to reduce communication rates from a sensor to the fusion center. Event-triggered estimators of

This paper addresses the harmonic problem in one-bit linear frequency modulation (LFM) signal recovery. We develop a novel quantization strategy with random thresholds to mitigate the annoying harmonic effect caused by one-bit sampling. The proposed quantization strategy changes the probability distribution of harmonic amplitude. In this case, the average amplitude of each order harmonic is dominated

Infrared small target detection is a crucial stage in many searching and tracking applications. Many tensor decomposition-based methods have achieved well performance in the scenes with uniform backgrounds and salient targets. However, the performance is potentially prone to be degraded when encountering highly complex scenes. It is mainly because the decomposition error caused by the sparse edge structures

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

The output signal-to-clutter-plus-noise ratio (SCNR) of space-time adaptive processing (STAP) decreases due to the dispersion of the transmit energy for traditional airborne multiple-input-multiple-output (MIMO) radar. Moreover the sufficient training samples cannot be provided to estimate the clutter covariance matrix (CCM) in the non-stationary environment. To solve these problems, a novel STAP method

The short-time linear canonical transform (STLCT) is a novel time-frequency analysis tool. In this paper, we generalize some different uncertainty principles for the STLCT of complex signals. Firstly, a new uncertainty principle for STLCT of complex signals in time and frequency domains is explored. Secondly, an uncertainty principle in two STLCT domains is obtained. They show that the lower bounds

This paper considers a massive multiple-input multiple-output (MIMO) wireless uplink communication system over Rayleigh fading channels. In this system, two single-antenna nodes timely upload data to a base station (BS) with a large number of antennas on the same time-frequency resources. The small scale fading coefficients keep constant during two consecutive time slots, after which they change into

For joint tracking and classification (JTC) of multiple extended targets in the presence of clutter and detection uncertainty, this paper proposes a recursive algorithm based on the probability hypothesis density (PHD) filter and star-convex random hypersurface model (RHM), resulting in the JTC-RHM-PHD filter. By modeling the extent state via the star-convex RHM, the JTC-RHM-PHD filter can classify

In this paper, a distributed extended Kalman filter (EKF) is developed for a class of nonlinear systems, whose outputs are measured by multiple sensors which send data using an event triggered mechanism through a communication network subject to loss and latency. Random transmission delay and multiple dropouts are modelled by a Bernoulli random sequence. The filter gains are determined in each sensor

The smooth variable structure filter (SVSF) is a type of sliding mode filter formulated in a predictor-corrector format and has seen significant development over the last 15 years. In this paper, we provide a comprehensive review of the SVSF and its variants. The developments, applications and improvements of the SVSF in terms of robustness and optimality are investigated. In addition, the combination

In this paper, we consider a two-way multiple-input multiple-output (MIMO) relaying system employing simplified space-time (ST) coding at both sources. The signals received at each source form a third-order tensor, which satisfies a parallel tucker2 (PARATUCK2) model. Exploiting this structure, we present a novel semiblind united alternating least squares (Uni-ALS) receiver for jointly estimating the

We present a transmit power adaptive filter-bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) waveform for joint communication and radar system. For frequency selective fading channels or frequency sensitive targets, a joint optimization problem is designed by taking both the radar detection performance and communication channel capacity into the objective function under the

While speech emotion recognition (SER) has been an active research field since the last three decades, the techniques that deal with the natural environment have only emerged in the last decade. These techniques have reduced the mismatch in the distribution of the training and testing data, which occurs due to the difference in speakers, texts, languages, and recording environments between the training

The classical sign algorithm (SA) has attracted much attention in many applications because of its low computational complexity and robustness against impulsive noise. However, its steady-state mean-square derivation (MSD) is large when a large step-size is used to guarantee a relatively fast convergence rate. To address this problem, the dual sign algorithm (DSA) was developed by using a piecewise

Robust Maximum Likelihood scheme for primary synchronization signal (PSS) detection and integer frequency offset (IFO) in Long Term Evolution (LTE) system has the best performance of cell search [5]. This scheme provides good synchronization performance based on correlation properties of Zadoff-Chu (ZC) sequences and reduced-rank representation of channel frequency response in multi-path propagation

In a multiple-input multiple-output (MIMO) radar, it is desired to maximize the power radiation in an angular region of interest, which, however, is hardly achievable because a set of orthogonal waveforms is employed at the engineering. Different from traditional colocated MIMO radar, space-time coding array (STCA) transmits an identical waveform with a tiny time delay circulating across array elements

Wavelet transform (WT) can be viewed as a differently scaled bandpass filter in the frequency domain, so WT is not the optimal time-frequency representation method for those signals which are not band-limited in the frequency domain. A novel fractional wavelet transform (FRWT) is proposed to break the limitation of WT, it displays the time and fractional frequency information jointly in the time-fractional-frequency

This article proposes an encoder-decoder network model for Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature. We make use of multiple low-level spectrogram features at the front-end, transformed into higher level features through a well-trained CNN-DNN front-end encoder. The high-level features and their combination (via a trained

In acoustic event recognition (AER), it is important to extract semantic features. As two crucial aspects of semantic features, the essential content and the temporal structure can strongly affect the understanding of humans and even computers. In this paper, we first divide each acoustic event sample into short segments. Then, for jointly considering the above two aspects, two semantic feature extraction

Tackling unsupervised source separation jointly with an additional inverse problem such as deconvolution is central for the analysis of multi-wavelength data. This becomes highly challenging when applied to large data sampled on the sphere such as those provided by wide-field observations in astrophysics, whose analysis requires the design of dedicated robust and yet effective algorithms. We therefore