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Maximum likelihood localization of a network of moving agents from ranges, bearings and velocity measurements Signal Process. (IF 4.4) Pub Date : 2024-03-19 Filipa Valdeira, Cláudia Soares, João Gomes
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Variable structure bidimensional reference pattern-based anti-bias track to track association Signal Process. (IF 4.4) Pub Date : 2024-03-19 Haohao Ren, Mo Tang, Lin Zou, Yun Zhou, Ming Li, Xuegang Wang
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A joint estimation algorithm for single-input multiple-output underwater acoustic communications Signal Process. (IF 4.4) Pub Date : 2024-03-19 Wentao Tong, Wei Ge, Xiao Han, Jingwei Yin
In single-input multiple-output (SIMO) underwater acoustic (UWA) communications, the receiver based on passive time reversal (PTR) combined with decision feedback equalizer (DFE) is widely used but has a limited performance. A multi-channel joint estimation algorithm based on sparse Bayesian learning (MJSBL) is proposed in this paper to exploit the diverse gain from multi-channels, where reasonable
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Novel Motion Parameter Estimation and Coherent Integration Algorithm for High Maneuvering Target with Jerk Motion Signal Process. (IF 4.4) Pub Date : 2024-03-16 Zhiyong Niu, Jibin Zheng, Tao Su
The high maneuvering target with jerk motion detection suffers from range migration (RM) and Doppler frequency migration (DFM). In this paper, the fourth order polynomial signal model is used to model the radar echo signal, and a novel high maneuvering target detection method is proposed. The RM is eliminated with Keystone transform (KT) and acceleration searching, and then the aligned echoes are extracted
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Fast dominant feature selection with compensation for efficient image steganalysis Signal Process. (IF 4.4) Pub Date : 2024-03-16 Xinquan Yu, Yuanyuan Ma, Yi Zhang, Xiaolong Li, Yao Zhao
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A Gaussian mixture multiple-model belief propagation filter for multisensor-multitarget tracking Signal Process. (IF 4.4) Pub Date : 2024-03-13 Feng Zheng, Yu Tian, Weicong Zhan, Jiancheng Yu, Kaizhou Liu
This paper presents a novel Gaussian mixture multi-model belief propagation (GMM-BP) filter for maneuvering multitarget tracking with multiple sensors. The filter is built upon the BP-based multisensor-multitarget tracking scheme, enabling accurate estimation of target numbers and states. It assumes linear Gaussian target motion, birth process, and sensor measurement models and utilizes the Gaussian
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Great-length wavelets on quantum computing platform: Applications and fast approximations Signal Process. (IF 4.4) Pub Date : 2024-03-13 Guangsheng Ma, Ziwei Zhou
Wavelets with great filter length, also known as high-order wavelets, exhibit favorable properties and superior performance in classical signal processing. We study the performance of great-length wavelets in the quantum computation of a dynamical model. Numerical results indicate that certain classical properties of wavelets can significantly improve their quantum localization capability. In particular
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Nonlinear acoustic echo cancellation based on pipelined Hermite filters Signal Process. (IF 4.4) Pub Date : 2024-03-13 Kai-Li Yin, Mhd Modar Halimeh, Yi-Fei Pu, Lu Lu, Walter Kellermann
This paper introduces a new class of nonlinear filters for nonlinear acoustic echo cancellation (NLAEC) based on Hermite nonlinear filters (HNFs), which is a sub-class of linear-in-the-parameters nonlinear filters (LIPNFs). Specifically, the basis functions of HNFs include cross-terms of the expanded inputs at different time instants, and are mutually orthogonal for white normally distributed input
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A robust active noise control system based on an exponential hyperbolic cosine norm Signal Process. (IF 4.4) Pub Date : 2024-03-12 Krishna Kumar, M.L.N.S. Karthik, Nithin V. George
The performance of conventional active noise control (ANC) systems degrades under non-Gaussian conditions. In the recent past, generalized filtered Maximum correntropy criterion (FxGMCC) algorithms has been widely used to tackle such non-Gaussian noises. However, noise reduction performance of MCC based algorithms degrades due to the high steady-state misalignment. To overcome this limitation, a filtered-x
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The knowledge-aided generalized multipath adaptive detector Signal Process. (IF 4.4) Pub Date : 2024-03-11 Chun Cao, Chongyi Fan, Jian Wang, Huagui Du, Xiaotao Huang
In urban environments, the challenges brought about by multipath propagation are significant for traditional adaptive radar detection problems. To tackle this issue, a generalized multipath signal model combined with environmental geometric prior information (EGPI) is developed. This model can be utilized regardless of whether the multipath signals are fully, partially, or partially-resolvable in the
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Phase retrieval from integrated intensity of auto-convolution Signal Process. (IF 4.4) Pub Date : 2024-03-11 Dan Rosen, Daniel Scarbrough, Jeff Squier, Michael B. Wakin
Ultra-fast optical pulses are the most ephemeral sensing paradigm ever devised, examining events over incredibly brief timescales with broadband illumination. A consequence of sensing at timescales lower than a picosecond is that pulse characterization cannot be done with traditional analog-to-digital samplers and must be ascertained from integrating intensity sensors. Techniques for pulse characterization
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Direction-of-arrival estimation in closely distributed array exploiting mixed-precision covariance matrices Signal Process. (IF 4.4) Pub Date : 2024-03-11 Yimin D. Zhang, Md Waqeeb T.S. Chowdhury
In this paper, we explore a collaborative direction-of-arrival (DOA) estimation technique that utilizes multiple closely spaced subarrays to maximize the potential of distributed arrays while minimizing communication overhead between the subarrays and the processing center. Each subarray computes its self-covariance matrix using the full-precision data and transmits it, along with a one-bit version
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Cauchy Kernel Minimum Error Entropy Centralized Fusion Filter Signal Process. (IF 4.4) Pub Date : 2024-03-10 Xiaoliang Feng, Changsheng Wu, Quanbo Ge
With the help of Information Theory Learning (ITL) theory, a kind of increasingly popular non-Gaussian filtering method has been designed in the sense of the maximum correntropy (MC) criterion or minimum error entropy (MEE) criterion. In MC or MEE criterion, Gaussian kernel function is usually chosen as the kernel function. Compared to the Gaussian kernel function, it is noted that the Cauchy kernel
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Continuous track-before-detect method for rotating radars Signal Process. (IF 4.4) Pub Date : 2024-03-09 Peiyuan Li, Guangxin Wu, Gui Li, Liangliang Wang, Gongjian Zhou
Mechanically steered scanning radars receive measurements in different azimuths sequentially rather than simultaneously for target detection and tracking. However, in conventional track-before-detect (TBD) methods, the requirement of waiting for all measurements of a whole scan, referring to a certain azimuth, leads to significant processing delays and boundary effect, which appears as that targets
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On the conjugate symmetry and sparsity of the harmonic decomposition of parametric surfaces with the randomised Kaczmarz method Signal Process. (IF 4.4) Pub Date : 2024-03-07 Mahmoud Shaqfa, Ketson R.M. dos Santos, Katrin Beyer
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Robust and sparse M-estimation of DOA Signal Process. (IF 4.4) Pub Date : 2024-03-07 Christoph F. Mecklenbräuker, Peter Gerstoft, Esa Ollila, Yongsung Park
A robust and sparse Direction of Arrival (DOA) estimator is derived for array data that follows a Complex Elliptically Symmetric (CES) distribution with zero-mean and finite second-order moments. The derivation allows to choose the loss function and four loss functions are discussed in detail: the Gauss loss which is the Maximum-Likelihood (ML) loss for the circularly symmetric complex Gaussian distribution
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A multi-level privacy-preserving scheme for extracting traffic images Signal Process. (IF 4.4) Pub Date : 2024-03-06 Xiaofei He, Lixiang Li, Haipeng Peng, Fenghua Tong
Traffic images are constantly used as a stick to assess traffic conditions. Traffic flow statistical analysis, road condition safety monitoring, vehicle violation detection, accident surveillance, and the driving environment perception of autonomous vehicles are all functionalities that depend on the processing of traffic images. However, traffic images contain privacy-sensitive information related
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Interference Suppression and Low Complexity Communication Reception for OOK-chirp Based RCC System Signal Process. (IF 4.4) Pub Date : 2024-03-05 Rui Xu, Ruiming Wen, Gang Li, Parfait Ifede Tebe, Yongjun Huang, Jian Li, Guangjun Wen
The existing radar-communication coexistence (RCC) waveform design based on chirp signal causes the increase of the complexity in the communication receiver, and multi-dimensional reuse also introduces more interference, leading to the deterioration of dual function performance. In this paper, a frequency division RCC system with on-off keying (OOK) based on frequency modulated continuous wave is designed
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NOMA communication system assisted with IRS and relay transmission Signal Process. (IF 4.4) Pub Date : 2024-03-05 Ashish, Preetam Kumar
Intelligent Reflecting Surfaces (IRS) have revolutionized wireless communication systems through their adaptive reconfiguration of the radio propagation environment. This paper compares an IRS-assisted non-orthogonal multiple access (NOMA) system and a relay-assisted NOMA system. The investigation delves into the impact of the number of IRS elements on the performance of a two-user NOMA system. A dynamic
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TDoA positioning with data-driven LoS inference in mmWave MIMO communications Signal Process. (IF 4.4) Pub Date : 2024-03-05 Fan Meng, Shengheng Liu, Songtao Gao, Yiming Yu, Cheng Zhang, Yongming Huang, Zhaohua Lu
Location awareness is an essential feature to support various mobile services, and cooperative positioning with channel state information (CSI) in millimeter wave multiple-input multiple-output (MIMO) networks is promising. Meanwhile, strong non-line of sight (NLoS) effects in outdoor scenarios severely reduce the model-based localization accuracy, and existing fingerprint-based methods have a critical
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A variable diagonal-matrix-step-size APA robust to impulsive noises Signal Process. (IF 4.4) Pub Date : 2024-03-05 Chan Park, Minho Lee, Taesu Park, PooGyeon Park
This study introduces a variable diagonal-matrix-step-size affine projection algorithm (APA), which shows robustness against to impulsive noises. Unlike the normal scalar step-size method, the independent step size for each input vector is used as the entry of the diagonal matrix, and the optimal step size at each time step is ascertained by conducting mean-square-deviation (MSD) analysis. In addition
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Robust Bayesian estimation via the [formula omitted]-divergence Signal Process. (IF 4.4) Pub Date : 2024-03-02 Yair Sorek, Koby Todros
In this paper, we introduce a novel framework for robust Bayesian parameter estimation using the -divergence. The framework incorporates an outlier resilient pseudo-posterior density function, called the -posterior, which is based on an empirical version of the -divergence. The latter involves utilizing Parzen’s non-parametric ernel density estimator to mitigate the influence of outliers. Under the
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MIMO radar partially correlated waveform design based on chirp rate diversity Signal Process. (IF 4.4) Pub Date : 2024-03-01 Ben Niu, Yongbo Zhao, Bangcong Ge, Tingxiao Zhang, Mei Zhang, Derui Tang
Waveform design is a significant topic in multiple-input-multiple-output (MIMO) radar. In this paper, we address the problem of waveform design for colocated narrowband MIMO radar transmit beampattern synthesis. The existing methods have not comprehensively considered the transmit beampattern synthesis under multiple practical constraints, such as constant modulus constraint, Doppler tolerance, high
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Hyperspectral sparse fusion using adaptive total variation regularization and superpixel-based weighted nuclear norm Signal Process. (IF 4.4) Pub Date : 2024-03-01 Jingjing Lu, Jun Zhang, Chao Wang, Chengzhi Deng
Many recent studies have shown that the adaptive total variation regularization has the advantage of better preserving local features of images compared with the celebrated total variation regularization. On the other hand, the superpixel-based weighted nuclear norm can compensate for the shortcomings of the superpixel-based standard nuclear norm, assigning different weights to singular values and
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An optimized denoised bias correction model with local pre-fitting function for weak boundary image segmentation Signal Process. (IF 4.4) Pub Date : 2024-03-01 Guina Wang, Zhen Li, Guirong Weng, Yiyang Chen
The active contour model (ACM) plays a paramount part in grasping visual properties of images and exacting targets of interest. It is overwhelming hardship for traditional ACMs to segment images with noise, intensity inhomogeneity or low contrast and consider computation speed for practical applicability. Therefore, an optimized denoised bias correction (ODBC) model incorporating the pre-piecewise
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Augmented co-prime array for enhanced DOA estimation with hole identification and filling strategies Signal Process. (IF 4.4) Pub Date : 2024-02-29 Lijun Huang, Qian Zhou, Shuhan Liao, Bin Gao, Shuhao Zhang, Lerong Hong
The co-prime array can effectively reduce mutual coupling, but the holes in difference co-array greatly decrease the uniform degrees of freedom (uDOFs). Hole filling is a considerable strategy to increase uDOFs, and it is critical for direction-of-arrival (DOA) estimation, since the inappropriate hole filling strategy can result in severer mutual coupling. To simultaneously increase DOFs and reduce
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The role of analog signal processing in upcoming telecommunication systems: Concept, challenges, and outlook Signal Process. (IF 4.4) Pub Date : 2024-02-28 Mir Mahdi Safari, Jafar Pourrostam
With the increasing demands in modern communications, the concepts of energy-efficient and low-cost processors have received a lot of attention from researchers in recent years. These cases are also taken into consideration in today’s high-speed and high-capacity communications (HSHCC), which is one of the requirements in new generation of wireless networks i.e., 6G. International telecommunication
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An adaptive unscented particle filter for a nonlinear fractional-order system with unknown fractional-order and unknown parameters Signal Process. (IF 4.4) Pub Date : 2024-02-27 Zhiyuan Jiao, Zhe Gao, Haoyu Chai, Shasha Xiao, Kai Jia
An unscented particle filter (UPF) is proposed for a nonlinear fractional-order system (NFOS) with an unknown order (UO) and unknown parameters. The Grünwald–Letnikov difference is used to discretize the continuous-time NFOS and the corresponding difference equation is acquired. For each sampled particle, a unscented transformation is applied, and the particles are afterwards optimized using a resampling
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A game model for semi-supervised subspace clustering with dynamic affinity and label learning Signal Process. (IF 4.4) Pub Date : 2024-02-24 Tingting Qi, Xiangchu Feng, Weiwei Wang
With the aid of partial supervised information, semi-supervised subspace clustering methods aim to obtain affinity matrices directly derived from raw data, and then those affinity matrices are utilized to get assignment matrices. These affinity matrices are susceptible to disturbances such as noise and outliers, which could significantly impact their quality. To mitigate this, it becomes essential
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Multivariate reduced rank regression by signal subspace matching Signal Process. (IF 4.4) Pub Date : 2024-02-24 Mati Wax, Amir Adler
We present a tuning-free and computationally simple solution for multivariate reduced rank regression, based on the recently introduced signal subspace matching (SSM) metric. Unlike the existing solutions, which solve simultaneously for the rank and the value of the coefficient matrix, our solution decouples the two tasks. First, the rank of the coefficient matrix is determined using the SSM metric
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An improved two-phase robust distributed Kalman filter Signal Process. (IF 4.4) Pub Date : 2024-02-24 Qinghua Luo, Shenghui Li, Xiaozhen Yan, Chenxu Wang, Zhiquan Zhou, Guangle Jia
To enhance the efficacy of the distributed filter in mitigating heavy-tailed non-Gaussian noise and accommodating intricate environments, this study investigates the utilization of absolute and relative measurement information for the realization of multi-target cooperative positioning and proposes an improved robust distributed Kalman filter in this paper. The method is divided into two phases. Firstly
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Robust adaptive beamforming algorithm based on coprime array with sensor gain-phase error Signal Process. (IF 4.4) Pub Date : 2024-02-22 Xiangdong Huang, Nian Hu, Xiaoqing Yang, Jian Huang
To diminish the performance degradation of coprime array beamforming arising from sensor gain and phase uncertainties, we propose a robust beamformer based on covariance matrix modification with augmented instrumental sensors. Initially, the received array data are split into two subarrays. Utilizing the instrumental sensors, we estimate the gain-phase errors of these two subarrays and then recombine
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Weighted omnidirectional semi-global stereo matching Signal Process. (IF 4.4) Pub Date : 2024-02-21 Penghui Bu, Hang Wang, Yihua Dou, Yan Wang, Tao Yang, Hong Zhao
Traditional semi-global matching (SGM) lacks interaction between scanlines and struggles to deal with the ambiguity of pixels in homogenous areas. In this paper, we propose a novel path-centering graph to perform weighted omnidirectional SGM (WOdSGM), in which the input image is divided into eight sub-trees, corresponding to eight optimization directions. In each pass, the outputs of pixels are recursively
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Multi-frame coherent track-before-detect method for weak tones in passive sonar Signal Process. (IF 4.4) Pub Date : 2024-02-20 Liu Zhang, Shengchun Piao, Junyuan Guo, Xiaohan Wang
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A Nyström-based low-rank unitary MVDR beamforming scheme Signal Process. (IF 4.4) Pub Date : 2024-02-19 Siyuan Jiang, Ming Jin, Shuai Liu, Zhiping Lin
A Nyström-based scheme is devised to approximate the real symmetrical covariance matrix of the popular unitary MVDR (U-MVDR) beamformer with low-rank kernel matrix, based on which a novel lightweight and robust Nyström-based U-MVDR (NU-MVDR) beamformer is developed. Subsequently, the full-dimensional Nyström covariance matrix estimator’s signal subspace can be calculated by eigenvalue decomposition
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Regularized maximum likelihood estimation for radio interferometric imaging in the presence of radiofrequency interferences Signal Process. (IF 4.4) Pub Date : 2024-02-17 Yassine Mhiri, Mohammed Nabil El Korso, Arnaud Breloy, Pascal Larzabal
We consider a regularized Maximum Likelihood Estimation (MLE) framework to produce images in the context of radio interferometric measurements. Specifically, we consider the class of compound Gaussian distributions to model the additive noise in the presence of radiofrequency interferences. In most cases, direct maximization of the likelihood is not tractable. To overcome this issue, we propose a generic
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Maximum likelihood based direction estimation for noncircular signals Signal Process. (IF 4.4) Pub Date : 2024-02-16 Y, a, n, g, -, H, o, , C, h, o, i
In the direction estimation for the signals incident on a sensor array, maximum likelihood (ML) based methods can provide superior performance than subspace based ones such as the multiple signal classification (MUSIC). When the incoming signals are noncircular the exploitation of the property can allow us to improve estimation performance. Based on the deterministic ML criterion, a direction estimation
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AP cooperation in Wi-Fi: Joint transmission with a novel precoding scheme, resilient to phase offsets between transmitters Signal Process. (IF 4.4) Pub Date : 2024-02-15 Yoav Levinbook, Doron Ezri, Ezer Melzer
Multi Access-Point (M-AP) cooperation is expected to play a key role in the next-generation Wi-Fi standard (namely the upcoming IEEE WLAN 802.11bn/UHR, dubbed Wi-Fi-8), particularly in dense deployments where inter-cell interference hinders further increase in network capacity. Among the various considered AP-cooperation techniques, coherent Joint Transmission (JT) is the most ambitious, aiming at
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A general robust approach for joint modeling of the family of scale mixture of Normal distribution Signal Process. (IF 4.4) Pub Date : 2024-02-15 Vinícius Silva Osterne Ribeiro, Lionel Bombrun, Juvêncio Santos Nobre, Charles Casimiro Cavalcante, Yannick Berthoumieu
In this paper, we present the class of linear models with errors belonging to the family of scale mixture of normal distributions, considering the reparameterization of the scatter matrix based on the Modified Cholesky Decomposition approach and that the mixing parameters of the model are deterministic but unknown and vary from each observation. Scoring functions and Hessian matrices are derived for
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Angle estimation based on Vandermonde constrained CP tensor decomposition for bistatic MIMO radar under spatially colored noise Signal Process. (IF 4.4) Pub Date : 2024-02-13 Jinli Chen, Yijun Tang, Xicheng Zhu, Jiaqiang Li
We address the problem of Vandermonde constrained CANDECOMP/PARAFAC (CP) tensor decomposition in application to angle estimation for bistatic multiple-input multiple output (MIMO) radar under spatially colored noise. By exploiting the temporally uncorrelated characteristic of colored noise, a new denoising scheme based on the temporally smoothed cross-correlation approach is presented. Then, after
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Robust image steganography against JPEG compression based on DCT residual modulation Signal Process. (IF 4.4) Pub Date : 2024-02-13 Yingkai Huang, Zhuxian Liu, Qiwen Wu, Xiaolong Liu
Robust steganography is the technology of hiding secret message in cover image so that the message can be recovered after additional image processing across social network. Traditional robust steganography schemes mainly relied on error correction code and embedding domain selection, which could not effectively address the additional errors introduced during JPEG compression. In this paper, we analyze
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Bi-orthogonality recovery and MIMO transmission for FBMC systems based on non-sinusoidal orthogonal transformation Signal Process. (IF 4.4) Pub Date : 2024-02-12 Ying Wang, Qiang Guo, Jianhong Xiang, Linyu Wang, Yang Liu
Filter Bank Multi-Carrier (FBMC) system based on offset Quadrature Amplitude Modulation (offset-QAM) combined with Multiple-Input-Multiple-Output (MIMO) technique faces great challenges. The inherent imaginary interference of FBMC seriously impacts the performance of the Maximum Likelihood (ML) detection technique in MIMO transmission. The application of the Alamouti code is also hindered. In this
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Analysis of a Diffusion LMS Algorithm with Probing Delays for Cyclostationary White Gaussian and Non-Gaussian Inputs Signal Process. (IF 4.4) Pub Date : 2024-02-11 Eweda Eweda, Jose C.M. Bermudez, Neil J. Bershad
The paper studies the behavior of the diffusion least mean square (DLMS) algorithm in the presence of delays in probing the unknown system by the nodes. The types of input distribution and the probing delays can be different for different nodes. The analysis is done for a network having a central combiner. This structure reduces the dimensionality of the resulting stochastic models while preserving
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Joint spatial, polarization, and temporal estimation based on multiple sparse Bayesian learning in GNSS multipath environments Signal Process. (IF 4.4) Pub Date : 2024-02-10 Ning Chang, Xi Hong, Wenjie Wang, Daniel Egea-Roca, José A. López-Salcedo, Gonzalo Seco-Granados
Global Navigation Satellite System (GNSS) suffers from the multipath signals reflected by various objects in the vicinity of receivers. A severe multipath environment may enormously hamper the tracking performance, resulting in meter-level pseudorange error. To solve the parameter estimation (angle, polarization, and time delay) problem and enhance multipath mitigation, particularly in the presence
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Mainlobe deceptive jammer suppression with DEPC-MIMO radar with joint transmit–receive design Signal Process. (IF 4.4) Pub Date : 2024-02-10 Jie Gao, Shengqi Zhu, Lan Lan, Ximin Li, Guisheng Liao
This paper investigates the problem of mainlobe deceptive jammer suppression via joint transmit–receive design in a multiple-input multiple-output (MIMO) radar. At the design stage, a discrete element-pulse coding (DEPC) phase is implemented in both the slow-time pulses and transmit array antennas. After decoding and compensation of the delayed pulses, the true and false targets can be identified in
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Robust secret image sharing scheme with improved anti-noise capability Signal Process. (IF 4.4) Pub Date : 2024-02-09 Shengyang Luo, Yaqi Liu, Xuehu Yan, Yuyuan Sun
Secret image sharing (SIS) is a promising image protection technology boasting high security and loss tolerance. However, shadow images generated by SIS are susceptible to noise contamination during real-world transmission, storage, and processing, resulting in data loss when recovering the secret image. Therefore, improving the robustness of SIS systems against noise is crucial. Unfortunately, there
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Discriminative subspace learning using generalized mean Signal Process. (IF 4.4) Pub Date : 2024-02-06 Jiyong Oh, Nojun Kwak
Linear discriminant analysis (LDA) is one of the most popular methods to extract discriminative features because it is simple and powerful. However, LDA fails to learn a discriminative subspace in some cases. This study deals with a problem of LDA, the so-called class separation (CS) problem, which means that some classes located close to each other in the original input space tend to overlap in a
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Asymptotically optimal procedures for sequential joint detection and estimation Signal Process. (IF 4.4) Pub Date : 2024-02-05 Dominik Reinhard, Michael Fauß, Abdelhak M. Zoubir
We investigate the problem of jointly testing multiple hypotheses and estimating a random parameter of the underlying distribution in a sequential setup. The aim is to jointly infer the true hypothesis and the true parameter while using on average as few samples as possible and keeping the detection and estimation errors below predefined levels. Based on mild assumptions on the underlying model, we
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Butterfly segmentation by multi scaled quantum cuts in agro-ecological environment Signal Process. (IF 4.4) Pub Date : 2024-02-05 Idir Filali, Mohamed Ramdani, Brahim Achour
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Byzantine-robust decentralized stochastic optimization with stochastic gradient noise-independent learning error Signal Process. (IF 4.4) Pub Date : 2024-02-03 Jie Peng, Weiyu Li, Qing Ling
This paper studies Byzantine-robust stochastic optimization over a decentralized network, where every agent periodically communicates with its neighbors to exchange local models, and then updates its own local model with one or a mini-batch of local samples. The performance of such a method is affected by an unknown number of Byzantine agents, which conduct adversarially during the optimization process
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Low complexity post-distorter based on extended kernel recursive least squares for visible light communications Signal Process. (IF 4.4) Pub Date : 2024-02-01 Jieling Wang, Menghan Li, Ba-Zhong Shen
In visible light communication (VLC) systems, the data transmission quality could be deteriorated by the nonlinear distortion, which stems from the inherent characteristic of light-emitting diode. In order to eliminate the influence of nonlinearity, reproducing kernel Hilbert space based post distortion technology is an effective solution. As a kind of kernel methods, extended kernel recursive least
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The motion model-based joint tracking and classification using TPHD and TCPHD filters Signal Process. (IF 4.4) Pub Date : 2024-02-01 Boxiang Zhang, Shaoxiu Wei, Wei Yi
This paper presents two new trajectory probability hypothesis density (TPHD) and trajectory cardinality probability hypothesis density (TCPHD) filters for joint tracking and classification (JTC), namely JTC-TPHD and JTC-TCPHD filters. We first introduce the classified trajectory RFS model to accommodate the motion model-based class information. The adaptation of the TPHD and TCPHD filters to the classified
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SCNet: Scattering center neural network for radar target recognition with incomplete target-aspects Signal Process. (IF 4.4) Pub Date : 2024-02-01 Qi Liu, Xinyu Zhang, Yongxiang Liu
Most of the previous radar automatic target recognition (RATR) methods based on high resolution range profile (HRRP) are designed under the assumption of complete target-aspects, which assumes the HRRP samples with different target-aspects are complete in training dataset or template library. Few works were concentrated on HRRP RATR with incomplete target-aspects. However, it is extremely difficult
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Tensor completion via joint reweighted tensor Q-nuclear norm for visual data recovery Signal Process. (IF 4.4) Pub Date : 2024-02-01 Xiaoyang Cheng, Weichao Kong, Xin Luo, Wenjin Qin, Feng Zhang, Jianjun Wang
Recently, the transform-based tensor nuclear norm methods have achieved encouraging results for low-rank tensor completion (LRTC) under the tensor singular value decomposition (t-SVD) framework. Among them, the tensor -nuclear norm, which uses a data-dependent matrix as transform, is more flexible than that of using fixed transform when handling different types of data. However, it only describes the
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Algorithms for improving the quality of underwater optical images: A comprehensive review Signal Process. (IF 4.4) Pub Date : 2024-01-30 Xuecheng Shuang, Jin Zhang, Yu Tian
High-quality underwater optical images are essential for various applications of underwater vision. However, these images often suffer from severe degradation, complex noise, low contrast, and color cast, leading to poor image quality. To address these issues and accomplish related underwater vision tasks more smoothly, researchers have made many efforts to improve the quality of underwater optical
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Learnable bilevel optimization method for electrical capacitance tomography Signal Process. (IF 4.4) Pub Date : 2024-01-30 Jing Lei, Qibin Liu
The positive role of electrical capacitance tomography technology depends on high-precision tomographic images. Despite its success, one of the main barriers is the low-quality tomogram. A new learnable bilevel optimization imaging method is proposed to address this problem in this study, in which the image prior and model parameters can be learned from the collected datasets. The upper level optimization
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Dual-functional radar-communication based on frequency modulated continuous wave exploiting constraint frequency hopping Signal Process. (IF 4.4) Pub Date : 2024-01-28 Rui Xu, Ruiming Wen, Gang Li, Chu Chu, Guangjun Wen
This paper focuses on the design of a frequency modulated continuous wave (FMCW) dual-functional radar-communication (DFRC) system with low-loss radar detection performance, aiming at achieving a balance among communication rate, symbol error rate (SER), and out-of-band leakage. For what the communication functionality concerns, the constrained frequency hopping (C–FH) sequence mapping and up/down
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A construction of multiple Z-complementary code sets with inter-set low correlation Signal Process. (IF 4.4) Pub Date : 2024-01-28 Nishant Kumar, Sushant K. Jha, Sudhan Majhi, Subhabrata Paul
This paper presents a construction of multiple Z-complementary code sets (ZCCSs) of prime power length with low inter-set cross-correlation values using extended generalized Boolean functions (EGBFs). By collecting the proposed multiple Z-complementary code sets (ZCCSs), a low correlation zone complementary sequence set (LCZ-CSS) with new parameters is also achieved as a byproduct of this construction
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A comparison of antenna placement criteria based on the Cramér–Rao and Barankin bounds for radio interferometer arrays Signal Process. (IF 4.4) Pub Date : 2024-01-25 Jianhua Wang, Lucien Bacharach, Pascal Larzabal, Mohammed Nabil El Korso
In this paper, we consider the problem of antenna placement for radio interferometer arrays. In this type of applications, signal-to-noise ratios (SNR) are typically low, and possibly lower than a SNR threshold under which the estimation performance of source parameters may degrade significantly. In this regime, the Cramér–Rao bound (CRB), which is often used for array design, is not a tight bound