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High-Accuracy DOA Estimation for Non-Collinear Sparse Uniform Array

IEEE signal processing LETTERS 2025年 32卷 206-210页

作者： Wang, Hongyong Chen, Xiaolong Deng, Weibo Zhang, Caisheng Xue, Yonghua Naval Aviat Univ Yantai 264001 Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China

Conventional sparse uniform arrays (SUAs) is composed of multiple identical and rigorously collinear uniform linear arrays. By adjusting the baseline length between the subarrays, the array aperture can be arbitrarily large, thus substantially improving the accuracy of the direction-of-arrival (DOA) estimation. However, in practical applications, it is challenging to meet the strict collinearity requirement due to geographical constraints. In this letter, to address this problem, we propose the non-collinear sparse uniform array (NCSUA) model to mitigate the influence of the non-ideal terrain and enhance the practicality of the SUA. A novel estimation algorithm is then proposed to resolve the angle ambiguity in NCSUA and effectively achieve high-accuracy DOA estimation. Compared with the conventional SUA, numerical simulation results demonstrate the superiority of NCSUA employing the new de-ambiguity algorithm in DOA estimation performance and practical applications.

关键词： Estimation signal processing algorithms Direction-of-arrival estimation Array signal processing Antenna arrays Accuracy Vectors Receiving antennas Radar Sparse matrices High-accuracy DOA estimation non-collinear array sparse uniform array

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Adaptive Adjustment for Laser Energy and PMT Gain Through Self-Feedback of Echo Data in Bathymetric LiDAR

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

作者： Zhou, Guoqing Song, Naihui Jia, Guoshuai Wu, Jinhuang Gao, Ke Huang, Jing Zhou, Xiang Xu, Jiasheng Lin, Tongzhi Zhang, Lieping Ma, Ran Guilin Univ Technol Coll Mech & Control Engn Guangxi Key Lab Spatial Informat & Geomatics Guilin 541004 Peoples R China Guilin Univ Technol Coll Geomatics & Geoinformat Guilin 541004 Peoples R China Guilin Univ Technol Coll Mech & Control Engn Guilin 541004 Peoples R China Guilin Univ Technol Guangxi Key Lab Spatial Informat & Geomatics Guilin 541004 Peoples R China Guilin Univ Technol Coll Phys & Elect Informat Engn Guilin 541004 Peoples R China Guilin Univ Technol Guangxi Key Lab Spatial Informat & Geomatics Guilin 541004 Peoples R China Guilin Univ Technol Coll Mech & Control Engn Guilin 541004 Peoples R China Tianjin Univ Ctr Remote Sensing Tianjin 300072 Peoples R China Guilin Univ Technol Coll Math & Stat Guilin 541004 Peoples R China Guilin Univ Aerosp Technol Guangxi Key Lab Special Engn Equipment & Control Guilin 541004 Peoples R China South Surveying & Mapping Technol Co Ltd Int Business Dept Guangzhou 510000 Peoples R China

Existing photomultiplier tube (PMT)-based gating control systems embedded in bathymetric light detection and ranging (LiDAR) devices cannot automatically adjust the PMT gain in real time, resulting in the saturation distortion of echo signals or failure to receive echo signals. Therefore, this study proposes an adaptive adjustment method for the laser energy and PMT gain based on the self-feedback of echo data from a high-speed sampling and storage system. Zynq-7000 All Programmable SoC (ZYNQ) was used as the primary controller, and the echo data analysis module, laser energy adjustment module, and PMT gain adjustment were designed based on the field-programmable gate array (FPGA). The corresponding control software was developed based on the advanced reduced instruction set computer machines (ARM) end to call the custom intellectual property (IP) cores. The proposed method was experimentally validated using indoor corridors and outdoor water areas, such as pools, reservoirs, Wujiu Beach, and the Beibu Gulf. The experimental results demonstrated that the proposed method could adaptively adjust laser energy and PMT gain, with which the voltage amplitude fluctuation range of the echo signal was effectively reduced from the original -1.7-0 V (acquisition range of high-speed sampling and storage system) to -1.04 to -0.26 V. With the comparative verification with multibeam sounder, the mean and standard deviation of the difference between the Z coordinates are -0.21 and 0.15, respectively, which indicates that the LiDAR using the proposed method achieves a similar accuracy of bathymetry.

关键词： Laser radar Lasers Software signal processing algorithms Field programmable gate arrays Fitting Pump lasers Adaptive adjustment bathymetry echo signal laser energy light detection and ranging (LiDAR) photomultiplier tube (PMT) gain

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Robust Distributed Estimation of Wireless Sensor Networks Under Adversarial Attacks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第5期73卷 7102-7113页

作者： Chen, Chao-Yang Tan, Dingrong Li, Pei Chen, Juan Gui, Guan Adebisi, Bamidele Gacanin, Haris Adachi, Fumiyuki Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China Manchester Metropolitan Univ Fac Sci & Engn Dept Engn Manchester M1 5GD England Rhein Westfal TH Aachen Inst Commun Technol & Embedded Syst D-52062 Aachen Germany Tohoku Univ Int Res Inst Disaster Sci IRIDeS Sendai Japan

This article focuses on the parameter estimation problem in wireless sensor networks (WSNs) under adversarial attacks, considering the complexities of sensing and communication in challenging environments. In order to mitigate the impact of these attacks on the network, we propose a novel AP-DLMS algorithm with adaptive threshold attack detection and malicious punishment mechanism. The adaptive threshold is constructed using the observation matrix and network topology to detect the location of malicious attacks, while the standard reference estimation is designed to obtain the estimated deviation of each node. To mitigate the impact of data tampering on network performance, we introduce the honesty factor and punishment factor to combine the weights of normal nodes and malicious nodes respectively. Additionally, we propose a new probabilistic random attack model. Simulations are conducted to investigate the influence of key parameters in the adaptive threshold on the performance of the proposed AP-DLMS algorithm, and the mean square performance of the algorithm is analyzed under various attack models. The results demonstrate that the proposed algorithm exhibits strong robustness in adversarial networks, and the proposed attack model effectively demonstrates the impact of attacks.

关键词： Estimation Wireless sensor networks Task analysis Sensors signal processing algorithms Adaptation models Wireless communication Distributed estimation wireless sensor networks adversarial attack detection robustness

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Tensor-Based Least-Squares Solutions for Multirelational signals and Applications

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第5期54卷 2852-2865页

作者： Chang, Shih-Yu Wu, Hsiao-Chun San Jose State Univ Dept Appl Data Sci San Jose CA 95192 USA Louisiana State Univ Sch Elect Engn & Comp Sci Baton Rouge LA 70803 USA Yuan Ze Univ Innovat Ctr AI Applicat Taoyuan 32003 Taiwan

The approach of least squares (LSs) has been quite popular and widely adopted for the common linear regression analysis, which can give rise to the solution to an arbitrary critically-, over-, or under-determined system. Such a linear regression analysis can be easily applied for linear estimation and equalization in signal processing for cybernetics. Nonetheless, the current LS approach for linear regression is unfortunately limited to the dimensionality of data, that is, the exact LS solution can involve only a data matrix. As the dimension of data increases and such data need to be represented by a tensor, the corresponding exact tensor-based LS (TLS) solution does not exist due to the lack of a pertinent mathematical framework. Lately, some alternatives such as tensor decomposition and tensor unfolding were proposed to approximate the TLS solutions to the linear regression problems involving tensor data, but these techniques cannot provide the exact or true TLS solution. In this work, we would like to make the first-ever attempt to present a new mathematical framework for facilitating the exact TLS solutions involving tensor data. To demonstrate the applicability of our proposed new scheme, numerical experiments regarding machine learning and robust speech recognition are illustrated and the associated memory and computational complexities are also studied.

关键词： Tensors Linear regression Cybernetics Speech recognition Machine learning signal processing algorithms Minimization High-dimensional linear regression joint polynomials-fitting robust speech recognition tensor inverse tensor-based least-squares (TLS) methods

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Dysfluent Speech Classification Using Variational Mode Decomposition and Complete Ensemble Empirical Mode Decomposition Techniques With NGCU-Based RNN

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IEEE ACCESS 2024年 12卷 174934-174953页

作者： Vinay, N. A. Vidyasagar, K. N. Rohith, S. Supreeth, S. Prasad, S. N. Kumar, S. Pramod Bharathi, S. H. Dayanand Sagar Coll Engn Dept Elect & Commun Engn Bengaluru 560078 India REVA Univ Sch Elect & Commun Engn Bengaluru 560064 India Nagarjuna Coll Engn & Technol Dept Elect & Commun Engn Bengaluru 562164 India REVA Univ Sch Comp Sci & Engn Bengaluru 560064 India Manipal Acad Higher Educ Manipal Inst Technol Bengaluru Dept Elect & Commun Manipal 576104 India JNN Coll Engn Dept Elect & Commun Shivamogga 577204 India

Dysfluency refers to discontinuity in speech due to noise or speech disorder, this dysfluency has unique features in terms of pitch and time based on these characteristics the dysfluent speech is categorized into repetition, prolongation, or blocking of words or phrases, and because of this uneven structure in speech, it named unstructured speech. The recognition and classification of such unstructured speech require an effective algorithm to boost the process of Automatic Speech Recognition in real-time and it is a challenging task because the system will take more time to extract features like pitch, frequency or time, and energy. From the past few decades, many researchers have proposed algorithms to address the mentioned, but still, there is a gap in recognizing the endpoint of each frame based on the mentioned which helps to robust the recognition accuracy of dysfluent speech. So, in this proposed work to extract the desired and unique features of dysfluent speech the samples are decomposed into different sub-signals by using two advanced methods of Empirical mode decomposition (EMD), they are Variational Mode Decomposition (VMD) and Complete Ensemble Empirical Mode Decomposition (CEEMD). These two techniques overcome the one major disadvantage of EMD that is the EMD techniques use different decomposition levels to produce the oscillation of the same time series signal. Hence combination of VMD and CEEMD decomposes the input speech sample into 5 sub-signals to classify the different classes of dysfluency using a New Gate Control Unit (NGCU) based on Recurrent Neural Networks (RNN).

关键词： Speech recognition Feature extraction Recurrent neural networks Logic gates Speech processing Frequency estimation Noise Empirical mode decomposition Time-frequency analysis signal processing algorithms ASR CEEMD EMD NGCU RNN VMD

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Beam-Delay Domain Channel Estimation for mmWave XL-MIMO Systems

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2024年第4期18卷 646-661页

作者： Hou, Hongwei He, Xuan Fang, Tianhao Yi, Xinping Wang, Wenjin Jin, Shi Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

This paper investigates the uplink channel estimation of the millimeter-wave (mmWave) extremely large-scale multiple-input-multiple-output (XL-MIMO) communication system in the beam-delay domain, taking into account the near-field and beam-squint effects due to the transmission bandwidth and array aperture growth. Specifically, we model spatial-frequency domain channels in the beam-delay domain to explore inter-antenna and inter-subcarrier correlations. Within this model, the frequency-dependent hybrid-field beam domain steering vectors are introduced to describe the near-field and beam-squint effects. The independent and non-identically distributed Bernoulli-Gaussian models with unknown prior hyperparameters are employed to capture the sparsity in the beam-delay domain, posing a challenge for channel estimation. Under the constrained Bethe free energy minimization framework, we design different structures and constraints on trial beliefs to develop hybrid message passing (HMP) algorithms, thus achieving efficient joint estimation of beam-delay domain channel and prior hyperparameters. To further improve the model accuracy, the multidimensional grid point perturbation (MDGPP)-based representation is presented, which assigns individual perturbation parameters to each multidimensional discrete grid. By treating the MDGPP parameters as unknown hyperparameters, we propose the two-stage HMP algorithm for MDGPP-based channel estimation, where the output of the initial stage is pruned for the refinement stage to reduce the computational complexity. Numerical simulations demonstrate the significant superiority of the proposed algorithm over benchmarks with both near-field and beam-squint effects.

关键词： Channel estimation Millimeter wave communication Symbols Estimation Vectors Antenna arrays signal processing algorithms millimeter-wave extremely large antenna array near-field effect beam-squint effect

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STAR-RIS in Cognitive Radio Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第12期23卷 19649-19666页

作者： Li, Haochen Liu, Yuanwei Mu, Xidong Chen, Yue Pan, Zhiwen You, Xiaohu Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211100 Peoples R China Queen Mary Univ London QMUL Sch Elect Engn & Comp Sci London E1 4NS England Kyung Hee Univ Dept Elect Engn Yongin 17104 South Korea Queens Univ Belfast Ctr Wireless Innovat CWI Belfast BT3 9DT North Ireland Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

The development of sixth-generation (6G) communication technologies is confronted with the significant challenge of spectrum resource shortage. To alleviate this issue, we propose a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided multiple-input multiple-output (MIMO) cognitive radio (CR) system. Specifically, the underlying secondary network in the proposed CR system reuses the same frequency resources occupied by the primary network with the help of the STAR-RIS. The secondary network sum rate maximization problem is first formulated for the STAR-RIS aided MIMO CR system. The adoption of STAR-RIS necessitates an intricate beamforming design for the considered system due to its large number of coupled coefficients. The block coordinate descent method is employed to address the formulated optimization problem. In each iteration, the beamformers at the secondary base station (SBS) are optimized by solving a quadratically constrained quadratic program (QCQP) problem. Concurrently, the STAR-RIS passive beamforming problem is resolved using tailored algorithms designed for the two phase-shift models: 1) For the independent phase-shift model, a successive convex approximation-based algorithm is proposed;2) For the coupled phase-shift model, a penalty dual decomposition-based algorithm is conceived, in which the phase shifts and amplitudes of the STAR-RIS elements are optimized using closed-form solutions. Simulation results show that: 1) The proposed STAR-RIS aided CR communication framework can significantly enhance the sum rate of the secondary system;2) The coupled phase-shift model results in limited performance degradation compared to the independent phase-shift model.

关键词： Array signal processing Reflection Protocols Interference Stars Cognitive radio Reconfigurable intelligent surfaces Simulation signal processing algorithms Security Simultaneously transmitting and reflecting reconfigurable intelligent surface cognitive radio beamforming

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Fully Distributed Cell-Free MIMO Systems: Architecture, Algorithm, and Testbed Experiments

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IEEE INTERNET OF THINGS JOURNAL 2024年第5期11卷 7956-7973页

作者： Lee, Mi Hyun Yun, Chaewon Kim, Geun Ho Park, Soo Young Yu, Chang Wahn Choi, Kae Won Sungkyunkwan Univ Sch Informat & Commun Engn Dept Elect & Comp Engn Suwon 16419 South Korea Elect & Telecommun Res Inst Mobile Commun Res Div Daejeon 34129 South Korea

In this article, we propose a new fully distributed cell-free multiple-input and multiple-output (MIMO) architecture that enables independent deployment of access points (APs) without any infrastructure such as a central processing unit (CPU). In the proposed architecture, all baseband processing is distributively executed by homogeneous APs without any CPU, thanks to the fully distributed functional split structure. Since the proposed architecture only consists of APs, we expect that a 5G/6G-based private network with a cell-free MIMO capability can easily be built for Industrial Internet of Things (IIoT) applications without the help of mobile network operators (MNOs). We also propose a novel precoding algorithm for orthogonal frequency division multiplexing (OFDM)-based cell-free MIMO systems. In the proposed precoding algorithm, the APs in a user-centric cluster cooperatively maximize the capacity of the target user equipment (UE) while minimizing the interference to nearby UEs. The proposed precoding algorithm solves the OFDM power allocation problem to allocate power on a per-subcarrier basis under the per- AP power constraints, which has not been addressed in previous works. We have built a full-fledged and real-time cell-free MIMO testbed that implements the proposed cell-free MIMO architecture and precoding algorithm. The validity of the proposed architecture and precoding algorithm is verified by experiments and simulations.

关键词： MIMO communication Precoding Computer architecture signal processing algorithms Clustering algorithms Central processing Unit OFDM Cell-free multiple-input and multiple-output (MIMO) functional split precoding private network

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Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 3272-3286页

作者： Shah, Shaik Basheeruddin Pradhan, Pradyumna Pu, Wei Randhi, Ramunaidu Rodrigues, Miguel R. D. Eldar, Yonina C. Weizmann Inst Sci IL-7610001 Rehovot Israel Indian Inst Petr & Energy Dept Humanities & Sci Visakhapatnam 530003 India Univ Elect Sci & Technol Chengdu 610054 Peoples R China UCL Dept Elect & Elect Engn London WC1H 9BT England

Solving linear inverse problems plays a crucial role in numerous applications. Algorithm unfolding based, model-aware data-driven approaches have gained significant attention for effectively addressing these problems. Learned iterative soft-thresholding algorithm (LISTA) and alternating direction method of multipliers compressive sensing network (ADMM-CSNet) are two widely used such approaches, based on ISTA and ADMM algorithms, respectively. In this work, we study optimization guarantees, i.e., achieving near-zero training loss with the increase in the number of learning epochs, for finite-layer unfolded networks such as LISTA and ADMM-CSNet with smooth soft-thresholding in an over-parameterized (OP) regime. We achieve this by leveraging a modified version of the Polyak-& Lstrok;ojasiewicz, denoted PL*, condition. Satisfying the PL* condition within a specific region of the loss landscape ensures the existence of a global minimum and exponential convergence from initialization using gradient descent based methods. Hence, we provide conditions, in terms of the network width and the number of training samples, on these unfolded networks for the PL* condition to hold, by deriving the Hessian spectral norm. Additionally, we show that the threshold on the number of training samples increases with the increase in the network width. Furthermore, we compare the threshold on training samples of unfolded networks with that of a standard fully-connected feed-forward network (FFNN) with smooth soft-thresholding non-linearity. We prove that unfolded networks have a higher threshold value than FFNN. Consequently, one can expect a better expected error for unfolded networks than FFNN.

关键词： Training Vectors Optimization Convergence Computational modeling signal processing algorithms Mathematical models Optimization guarantees algorithm unfolding LISTA ADMM-CSNet Polyak-& Lstrok ojasiewicz condition

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Towards Applicable Unsupervised signal Denoising via Subsequence Splitting and Blind Spot Network

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 4967-4982页

作者： Wang, Ziqi Cao, Zihan Xie, Julan Li, Huiyong He, Zishu Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 610000 Peoples R China Univ Elect Sci & Technol China Dept Math Chengdu 610000 Peoples R China

Denoising is a significant preprocessing process, garnering substantial attention across various signal-processing domains. Many traditional denoising methods assume signal stationary and adherence of noise to Gaussian distribution, thereby limiting their practical applicability. Despite significant advancements in machine learning and deep learning methods, machine learning-based (ML-based) approaches still require manual feature engineering and intricate parameter tuning, and deep learning-based (DL-based) methods, remain largely constrained by supervised denoising techniques. In this paper, we propose an unsupervised denoising approach that addresses the shortcomings of previous methods. Our proposed method uses subsequence splitting and blind spot network to adaptively learn the signal characteristics in different scenarios, so as to achieve the purpose of denoising. The experimental results show that our method performs satisfactorily on both single-sensor and array signal denoising problems under Gaussian white noise and Impulsive noise. Moreover, our method is also verified to be effective on some array signal processing problems of Direction of Arrival (DOA) estimation, Estimated Number of Sources, and Spatial Spectrum estimation. Finally, in the discussion experiments and generalization experiments, we demonstrate that our method performs well across a wide variety of array forms and degrees of signal correlation, and has good generalization. Our code will be released after possible acceptance.

关键词： Noise reduction signal denoising Array signal processing Noise Sensor arrays Wavelet transforms signal processing algorithms Noise measurement Wiener filters Support vector machines unsupervised inference array forms Gaussian noise and Impulsive noise

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