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

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

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

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

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

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

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

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

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

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

Most of the local descriptors exists today are based on the relationship between the neighborhood pixels and the center pixel by considering the 3×3 spatial window. Moving one step forward from the previous methodologies used, this research paper introduces the novel descriptor for Face analysis called as orthogonal difference-Local binary pattern (OD-LBP). In OD-LBP, initially the 3 gray level differences

Specific emitter identification (SEI) is an important problem in the field of electronic intelligence. There are two major limitations in most existing SEI methods: First, the features should be artificially extracted, which requires specialized expertise; Second, various features are not merged effectively to improve performance. In this paper, an automatic multi-feature extraction and fusion method

Infrared imaging has been an efficient anti-drone approach due to its low-cost, anti-interference and all-weather working characteristics. However, the detection of Unmanned Aerial Vehicle (UAV) through infrared camera is still a challenging issue because infrared targets in the field-of-view are usually small and lack of shape and texture features. In this paper, we propose an infrared small target

A large portion of the Internet bandwidth is used for transmission of multimedia such as audio data. As the file sizes are usually much bigger than the maximum network packet size, the audio data are segmented into fragments. For eavesdropping or network surveillance purposes, the first step of a sniffer may be to determine the codec by which a fragment is generated. This problem is usually modeled

For the problem of inaccurate or difficult to obtain statistical characteristics of non-Gaussian noise, an interacting T-S fuzzy modeling algorithm is proposed to incorporate spatial-temporal information into particle filtering. In the proposed method, feature information is characterized by multiple semantic fuzzy sets, and the model transition probabilities are estimated by using the fuzzy set transition

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

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

Direction of arrival (DOA) estimation is one of the most important techniques applied in many practical engineering applications. Multiple signal classification (MUSIC) has gained increasing attention due to its high resolution in space. Many methods have been studied to handle the noise of Alpha-stable distribution within the framework of non-Gaussian signal processing. Inspired by a state-of-the-art

The empirical mode decomposition (EMD) is a method that is commonly applied to extract the intrinsic mode functions (IMFs) of a signal by a sifting process, which requires imposing the extended extrema at both ends of the signal (i.e., the end condition). The imposition of extended extrema can cause an error, which is often presented by the changing shapes of original envelopes and distort extracted

Seismic signal analysis is the main step in data processing through the petroleum exploration via costly seismic investigations. Precision of target delineation by seismic data for exploratory drilling strongly depends on resolution of the seismic image. However, resolution of the seismic image is restricted by the band-limited nature of the seismic signal and inherent deficiencies in signal enhancement

Symmetric regularized picture fuzzy clustering is a new fuzzy clustering method, and it is difficult to choose its weighting fuzzy factor m and lacks certain robustness to noises or outliers. To this end, this paper proposes a robust entropy-based symmetric regularized picture fuzzy clustering with spatial information constraints for noisy image segmentation. The idea of maximum entropy fuzzy clustering

Sparse Code Multiple Access (SCMA) is a multi-dimensional codebook based on a class of Non-Orthogonal Multiple Access (NOMA) to provide many users through non-orthogonal resource elements without detection complexity in 5G wireless communications. The codebook design is one of the main criteria in SCMA downlink systems. This paper proposes an efficient SCMA joint codebook design and assignment model

Physical layer security is a way to realize the secure transmission of information by employing the characteristics of wireless channels. Thanks to its spatial and temporal focusing property, the time-reversal (TR) transmission can achieve effective secure communications even when the transmitter is only equipped with one antenna. This paper studies the design and optimization of the security scheme

This study evaluates the performance of single-input multiple-output (SIMO) wireless networks or dual-branch (DB) SIMO scenarios over the η–μ and the λ–μ fading links. First, the probability density functions (PDFs) of the instantaneous signal to noise ratio for a DB SIMO system in the η–μ and the λ–μ fading conditions. Then, the closed-form expressions of the effective rate (ER or effective capacity)

The problem of uncooperative source localization and synchronization in asynchronous sensor networks has been considered previously in a centralized manner, where all the raw measurements are delivered to a processing center. However, for large-scale sensor networks the transmission of raw measurements over the networks requires considerable communication overhead, besides being vulnerable to transmission

In this work, we have analyzed the pitch robustness of the recently reported power normalized cepstral coefficient (PNCC) feature for noise robust children's speech recognition. The PNCC feature is intended to suppress various types of common additive noise present in speech data. The PNCC feature is noted to be susceptible to pitch variations. In order to normalize the pitch effect on PNCC, a pitch

Several emerging application scenarios for the multi-hop wireless networking have been introduced in 5G and beyond networks, to provide ultra-low latency and much higher data rate to the end users. In this work, an integrated performance evaluation of a decode-and-forward (DF) multi-hop wireless communication system is undertaken over the non-linear generalized α−κ−μ and α−κ−μ-Extreme fading models

This paper considers jointly designing the digital beamformer (DBF) and analog beamformer (ABF) for multi-user downlink hybrid beamforming systems in order to enhance communication performance and reduce information leakage. Our goal is maximizing the minimum signal-to-interference-plus-noise ratio (SINR) while depressing the maximum sidelobe level (SLL) of all users. Since the problem is difficult

The least squares based affine projection algorithm (APA) is sensitive to outliers/impulsive noise in the desired data. A novel robust APA over distributed networks scenario is proposed in this manuscript, which is based on the rank based robust estimator named Wilcoxon norm. The proposed diffusion Wilcoxon affine projection algorithm (DWx-APA) based on pseudo least squares formulation is robust against

In this paper, we propose a new MIMO communication system in a time-varying multipath environment, using a Khatri-Rao space-time (KRST) coding combined with a multiple Khatri-Rao product of symbol matrices (MKRSM). It is shown the signals received at the receiver form a tensor which satisfies a (M+2)-order nested PARAFAC model, where (M−1) denotes the number of symbol matrices considered for MKRSM

With the advances in electronic countermeasures (ECMS), especially the emergence and development of active jammers, there is an urgent demand for anti-jamming techniques. In this paper, we proposed a cognitive active anti-jamming method based on frequency diverse array (FDA) radar phase center. For the uniform linear FDA (ULFDA) radar, we derive the closed form of phase center, based on which the regulation

Combining segmentation, classification, and time-fractional diffusion filtering, an excellent smoothing method of peak-preserving is proposed. First, the signal is divided into equal-length segments. Second, these segments are classified according to the similarity. Third, similar segments in the same class are stacked into a two-dimensional array, and then they are filtered by the two-dimensional

The non-linear frequency modulation (NLFM) waveform is one of the existing waveforms that can be used in high range resolution radar applications. However, a high sampling frequency and consequently an expensive ADC are required. To overcome this drawback while taking advantage of the features of the NLFM waveform, we suggest approximating the wideband NLFM waveform by a piecewise linear waveform and

This paper addresses the multichannel adaptive detection of signal with unknown amplitude in the presence of coloured Gaussian interference. The interference is modelled as an autoregressive (AR) process with regard to its spatial and temporal correlation. We propose a new parametric detector in which the ability to reject fictitious targets is considered by including a mismatched signal in the null

This paper proposes the beta binomial autoregressive moving average model (BBARMA) for modeling quantized amplitude data and bounded count data. The BBARMA model estimates the conditional mean of a beta binomial distributed variable observed over the time by a dynamic structure including: (i) autoregressive and moving average terms; (ii) a set of regressors; and (iii) a link function. Besides introducing

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

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

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

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

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

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