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Ridgelet Transform Based on Optimal Basic Wavelet and Its Application in Seismic Discontinuity Detection

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Zhao, Liang Gao, Jinghuai Li, Zhen Tian, Yajun Zhao, Haoqi Yang, Tao Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China

High-dimensional time-frequency (TF) transforms are essential tools in seismic data processing. However, commonly used transforms such as Ridgelet, Curvelet, and Contourlet exhibit limitations in time-shifting invariance and basis function selection, which impacts on their effectiveness in seismic data analysis. To address these limitations, this study introduces optimal basic wavelet (OBW)-Ridgelet, a novel approach integrating the OBW with the Ridgelet transform. By combining OBW with Ridgelet, this method aims to enhance the TF localization for seismic structural analysis and time-shifting invariance property. We also present a workflow for seismic discontinuity detection, employing the C3 algorithm to the decomposed seismic data to get multiscale coherence and introduce the similarity coefficient for scale selection of the multiscale coherence. Synthetic and field data examples demonstrate the effectiveness and robustness of the proposed method, yielding promising results for seismic signal interpretation. The integration of OBW-Ridgelet enriches the toolkit for seismic signal analysis and holds the potential for refining seismic feature detection and interpretation in practical applications.

关键词： Transforms Continuous wavelet transforms Coherence Wavelet analysis Location awareness Sparse approximation signal processing algorithms Optimal basic wavelet (OBW) Ridgelet transform three-parameter wavelet (TPW)

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A New Iterative Weighted Least Squares Algorithm for 1-D SA Localization

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IEEE signal processing LETTERS 2025年 32卷 1740-1744页

作者： Zou, Yanbin Xiao, Yangpeng Wu, Wenbo Sun, Yimao Liu, Huaping Shantou Univ Dept Elect & Informat Engn Shantou 515063 Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA

Three-dimensional (3-D) target localization using one-dimensional (1-D) space angle (SA) measurements from linear arrays has recently gained significant attention. Each 1-D SA measurement defines a conical surface, and the intersection of multiple such surfaces determines the target's location in 3-D space. However, state-of-the-art methods for solving the 1-D SA localization problem are often either suboptimal or computationally intensive. In this paper, we propose a novel iterative weighted least squares (IWLS) algorithm to address the problem. To provide deeper insights, we present a geometric interpretation of the iterative process, highlighting its physical significance. Furthermore, we analyze the computational complexity of the proposed algorithm and compare it with existing methods. Simulation results demonstrate that the proposed algorithm not only achieves higher estimation accuracy but also requires less computational time compared to state-of-the-art approaches.

关键词： signal processing algorithms Location awareness Three-dimensional displays Vectors Sun Noise measurement Iterative algorithms Convergence Planar arrays Training One-dimensional space angle (1-D SA) iterative weighted least squares (IWLS)

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Parameterized Separate Channel Reconstruction in RIS-Assisted Multi-User MIMO OFDM Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第12期73卷 19125-19139页

作者： Ling, Taiyang Han, Yu Li, Xiao Tian, Jiachen Jin, Shi Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

The channel estimation overhead problem is a key challenge for the reconfigurable intelligent surface (RIS)-assisted wireless communication systems. In this paper, we analyze the spatial consistency of multi-users for RIS-assisted multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems and classify users in each user group into two kinds, namely one central user (CUE) and several adjacent users (AUE). We propose an innovative two-phase multi-user joint parameter estimation (MJPE) scheme. In the first phase, we analyze the "orthogonality" of the CUEs in the UE-RIS channel and the "coherence" in the RIS-BS channel and propose a joint concurrent transmission (JCT) algorithm and a joint Newton orthogonal matching pursuit (JNOMP) algorithm, respectively. In the second phase, we propose an extrapolation algorithm for UE-RIS channel estimation based on the spatial proximity of adjacent users around the central users. In this scheme, separated channel parameters are estimated for each path for each user, including sparse paths for UE-RIS channels and non-line-of-sight (NLoS) paths for RIS-BS channel, based on known line-of-sight (LoS) path for RIS-BS channel. A new pilot protocol with an RIS phase control strategy is proposed, which can effectively reduce pilot overhead. In addition, we analyze the time computational complexity of the algorithm. The numerical results demonstrate that the proposed multi-user estimation algorithm can accurately accomplish channel reconstruction with low overhead.

关键词： Channel estimation Estimation OFDM Matching pursuit algorithms Array signal processing signal processing algorithms Parameterized channel estimation reconfigurable intelligent surface multi-user multi-user

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Instantaneous Frequency and Amplitude Estimation in Multicomponent signals Using an EM-Based Algorithm

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 1130-1140页

作者： Legros, Quentin Fourer, Dominique Meignen, Sylvain Colominas, Marcelo A. Univ Grenoble Alpes Jean Kuntzmann Lab Grenoble F-38401 France Univ Evry Paris Saclay IBISC Lab F-91020 Courcouronnes France Consejo Nacl Invest Cient & Tecn Inst Res & Dev Bioengn & Bioinformat IBB RA-3100 Entre Rios Argentina Univ Nacl Entre Rios UNER Fac Ingn Oro Verde RA-3100 Entre Rios Argentina

This paper addresses the problem of estimating the instantaneous frequency (IF) and amplitude of the modes composing a non-stationary multicomponent signal in the presence of noise. A novel observation model for the signal spectrogram is developed within a Bayesian framework to handle intricate configurations involving noise or overlapping components. The model parameters are estimated using a stochastic variant of the Expectation-Maximization algorithm, bypassing the computationally expensive joint parameter estimation from the posterior distribution. We then design an algorithm for instantaneous amplitude and frequency estimation that accounts for overlap and amplitude variations of the components. To assess the performance of the proposed method, we conduct experiments on both real-world and simulated signals, involving separated or crossing modes. The benefits of our method in terms of efficiency compared with several state-of-the art techniques appear to be significant in that latter case, but also when the amplitude of the components are varying across time.

关键词： signal processing algorithms Estimation Bayes methods Time-frequency analysis Spectrogram Frequency estimation TV Ridge extraction time-frequency Bayesian estimation expectation-maximization algorithm Monte Carlo sampling

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An Efficient Benders Decomposition Approach for Optimal Large-Scale Network Slicing

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 4935-4949页

作者： Chen, Wei-Kun Wu, Zheyu Zhang, Rui-Jin Liu, Ya-Feng Dai, Yu-Hong Luo, Zhi-Quan Beijing Inst Technol Sch Math & Stat Beijing Key Lab MCAACI Beijing 100081 Peoples R China Chinese Acad Sci Inst Computat Math & Sci Engn Comp Acad Math & Syst Sci State Key Lab Sci & Engn Comp Beijing 100190 Peoples R China Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China

This paper considers the network slicing (NS) problem which attempts to map multiple customized virtual network requests to a common shared network infrastructure and allocate network resources to meet diverse service requirements. This paper proposes an efficient customized Benders decomposition algorithm for globally solving the large-scale NP-hard NS problem. The proposed algorithm decomposes the hard NS problem into two relatively easy function placement (FP) and traffic routing (TR) subproblems and iteratively solves them enabling the information feedback between each other, which makes it particularly suitable to solve large-scale problems. Specifically, the FP subproblem is to place service functions into cloud nodes in the network, and solving it can return a function placement strategy based on which the TR subproblem is defined;and the TR subproblem is to find paths connecting two nodes hosting two adjacent functions in the network, and solving it can either verify that the solution of the FP subproblem is an optimal solution of the original problem, or return a valid inequality to the FP subproblem that cuts off the current infeasible solution. The proposed algorithm is guaranteed to find the globally optimal solution of the NS problem. By taking the special structure of the NS problem into consideration, we successfully develop two families of valid inequalities that render the proposed algorithm converge much more quickly and thus much more efficient. Numerical results demonstrate that the proposed valid inequalities effectively accelerate the convergence of the decomposition algorithm, and the proposed algorithm significantly outperforms the existing algorithms in terms of both solution efficiency and quality.

关键词： signal processing algorithms Heuristic algorithms Routing Cloud computing Classification algorithms Network slicing Convergence Standards Resource management Network function virtualization Benders decomposition Farkas' Lemma network slicing resource allocation valid inequality

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Data-driven efficient digital signal processing over a field trial space-division multiplexed fiber-optic transmission 31

Data-driven efficient digital signal processing over a field...

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31st International Conference on Computer Communications and Networks (ICCCN)

作者： Reyes, Luis Felipe Florenzan Smarral, Francesco Ryf, Roland Hayashi, Tetsuya Marotta, Andrea Antonelli, Cristian D'Innocenzo, Alessandro Univ Aquila Dept Informat Engn Comp Sci & Math I-67100 Laquila AQ Italy Nokia Bell Labs 791 Holmdel Rd Holmdel NJ 07733 USA Univ Aquila Dept Phys & Chem Sci I-67100 Laquila AQ Italy Sumitomo Elect Ind Ltd 1 TayachoSakaeku Yokohama Kanagawa 2448588 Japan

ISBN: (数字)9781665497268

ISBN: (纸本)9781665497268

This paper discusses the use of data-driven methodologies to improve multiple-input multiple-output (MIMO) Digital signal processing (DSP) performance for crosstalk cancelation in optical systems supporting multiple spatial modes. A crucial problem in time-domain equalization is the large quantity of data needed to compensate the effect of the crosstalk when the signals cover a large number of kilometers. In this respect, the study of techniques that are able to reduce the amount of information needed to perform equalization is addressed, and a reduction algorithm based on the Principal Components Analysis (PCA) and cross-correlation analysis to improve traditional equalizers' performance is proposed. Experimental validation is performed over the world-first space-division multiplexed (SDM) field trial.

关键词： Multiplexing Optical fibers Urban areas signal processing algorithms Crosstalk Digital signal processing Optical fiber networks

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Wideband MIMO Radar Transmit Beampattern Synthesis in a Spectrally Crowded Environment

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第4期60卷 4044-4057页

作者： Jia, Congyue Su, Hongtao Wang, Zhaoyi Jing, Xinchen Mao, Zhi Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Nanjing Res Inst Elect Technol Nanjing 211167 Peoples R China

The increased electronic equipment makes the spectrum occupied by wireless applications more crowded, seriously weakening the detection performance of the radar. This article addresses the problem of transmit beampattern synthesis for wideband multiple-input multiple-out (MIMO) radar facing the same frequency electromagnetic conflict. According to the prior information about the environment, the spectral constraints are forced on the radar waveform to flexibly control the energy in the specified interference frequency band and direction. Meanwhile, low peak-to-average power ratio (PAR) constraints are added to the radar waveform to achieve high transmission efficiency. To solve this resultant nonconvex multiconstraint beampattern matching design problem, this article proposes an iterative algorithm that simplifies the problem by introducing double auxiliary variables (DAV) and then solves it based on the alternating direction method of multipliers (ADMM) framework. These auxiliary variables allow us to directly control the radar radiation energy at each discrete space-frequency point. In particular, the proposed DAV-ADMM algorithm can guarantee convergence with bounded penalty parameters. Finally, the effectiveness of the algorithm is verified by numerical simulations in various challenging scenarios.

关键词： Radar Wideband Interference signal processing algorithms Frequency control MIMO radar Peak to average power ratio Multiple-input multiple-out (MIMO) radar peak-to-average power ratio (PAR) constraint space-frequency nulling waveform design wideband beampattern

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Efficient Dual-Scale Generalized Radon-Fourier Transform Detector Family for Long Time Coherent Integration

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 4237-4252页

作者： Li, Suqi Wang, Yihan Wang, Bailu Battistelli, Giorgio Chisci, Luigi Cui, Guolong Chongqing Univ Sch Microelect & Commun Engn Chongqing 401331 Peoples R China Univ Firenze Dipartimento Ingn Informaz DINFO I-50134 Florence Italy Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

Long Time Coherent Integration (LTCI) is an effective method for weak target detection, since it can accumulate target energy through long time observation, thus improving the target output signal-to-noise ratio (SNR). However, for a moving target, defocusing can occur due to range migration (RM) and Doppler frequency migration (DFM). To address this issue, RM and DFM corrections are required in order to achieve a well-focused image for the subsequent detection. Since RM and DFM are induced by the same motion parameters, existing approaches such as the generalized Radon-Fourier transform (GRFT) or the keystone transform (KT)-matching filter process (MFP) adopt the same search space for the motion parameters in order to eliminate both effects, thus leading to large redundancy in computation. To this end, this paper first proposes a dual-scale decomposition of the target motion parameters, consisting of well designed coarse and fine motion parameters. Then, utilizing this decomposition, the joint correction of the RM and DFM effects is decoupled into a cascade procedure, first RM correction on the coarse search space and then DFM correction on the fine search spaces. As such, step size of the search space can be tailored to RM and DFM corrections, respectively, thus avoiding large redundant computation effectively. The resulting algorithms are called dual-scale GRFT (DS-GRFT) or dual-scale KT-MFP (DS-KT-MFP) which provide comparable performance while achieving significant improvement in computational efficiency compared to standard GRFT (KT-MFP). Simulation experiments verify their effectiveness and efficiency.

关键词： Detectors Couplings Computational efficiency Time-domain analysis Time-frequency analysis Standards signal processing algorithms Dual scale decomposition doppler frequency migration (DFM) generalized radon-fourier transform (GRFT) long time coherent integration (LTCI) range migration (RM)

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A Distributed Adaptive Algorithm for Non-Smooth Spatial Filtering Problems in Wireless Sensor Networks

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 4682-4697页

作者： Hovine, Charles Bertrand, Alexander Katholieke Univ Leuven STADIUS Ctr Dynam Syst Signal Proc & Data Analyt B-3001 Leuven Belgium Katholieke Univ Leuven Leuven AI Inst Artificial Intelligence B-3001 Leuven Belgium

A wireless sensor network often relies on a fusion center to process the data collected by each of its sensing nodes. Such an approach relies on the continuous transmission of raw data to the fusion center, which typically has a major impact on the sensors' battery life. To address this issue in the particular context of spatial filtering and signal fusion problems, we recently proposed the Distributed Adaptive signal Fusion (DASF) algorithm, which distributively computes a spatial filter expressed as the solution of a smooth optimization problem involving the network-wide sensor signal statistics. In this work, we show that the DASF algorithm can be extended to compute the filters associated with a certain class of non-smooth optimization problems. This extension makes the addition of sparsity-inducing norms to the problem's cost function possible, allowing sensor selection to be performed in a distributed fashion, alongside the filtering task of interest, thereby further reducing the network's energy consumption. We provide a description of the algorithm, prove its convergence, and validate its performance and solution tracking capabilities with numerical experiments.

关键词： signal processing algorithms Wireless sensor networks Optimization Spatial filters Linear programming Europe Distributed databases Real-time systems Convergence Adaptive systems distributed signal processing non-smooth optimization spatial filtering

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Favor the Tortoise Over the Hare: An Efficient Detection Algorithm for Cooperative Networks

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 3153-3170页

作者： Feitosa, Allan E. Nascimento, Vitor H. Lopes, Cassio G. Univ Sao Paulo Dept Elect & Syst BR-05508010 Sao Paulo Brazil

We develop a low-cost algorithm to decide the current state of an environment being monitored by a cooperative and fully distributed wireless network of intelligent sensors, with a low time to reach a given performance. We consider WSNs (Wireless Sensor Networks) deployed under stringent power conditions, a situation for which low computational complexity and low power consumption is highly desired. We model a multiple hypothesis test using the diffusion Least Mean Square (dLMS) algorithm, a well known estimation technique used in distributed networks, to process data and also share information among nodes across the network. Our first contribution is showing that the performance of this theoretical detector, given in terms of the average probability of error, approximates the optimal performance if the underlying estimator operates at a slow learning rate, which is achieved by a sufficiently small step size. Notably, the detector performance improves as the value of the this step size is reduced, without any reduction in the detection error convergence rate, despite the slower estimation convergence rate. This somewhat counter-intuitive behavior is explained theoretically and confirmed by simulations. From this theoretical formulation, we devise a new detector with low computational complexity whose performance also closely matches that of the optimal and shows the same aforementioned behavior, where the slowest learning rate provides the best detection performance in terms of both the probability of error and convergence rate. We also show that this performance can be easily achieved provided that an adequate initialization of the estimation algorithm is chosen.

关键词： signal processing algorithms Sensors Wireless sensor networks Vectors Task analysis Estimation Environmental monitoring Distributed detection wireless sensor networks detection in WSN diffusion-LMS Bayesian detection

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