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Beamspace Channel Estimation via PARAFAC Decomposition for RIS Assisted Millimeter-Wave Multiuser MISO Communications

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第5期74卷 7594-7609页

作者： Guo, Xinying Xie, Zongyuan Zhang, Jiankang Chen, Sheng Zhu, Chunhua Henan Univ Technol Key Lab Grain Informat Proc & Control Minist Educ Zhengzhou 450001 Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Peoples R China Bournemouth Univ Dept Comp & Informat Poole BH12 5BB England Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England

The reconfigurable intelligent surface (RIS) with massive low-cost passive reflecting elements integrated on a planar surface has the ability to favourably reconfigure the wireless propagation environment, thereby significantly improving the performance of wireless communication networks. In this work, we consider uplink (UL) channel estimation for the RIS assisted millimeter-wave multiuser multiple-input single-output beamspace system where the base station (BS) is equipped with lens antenna array. This channel state information (CSI) estimation task is extremely challenging for two reasons. First, the BS only has limited number of radio frequency chains but the size of beamspace channel is very large. Second, the number of passive components in the RIS is abundance but they lack signal processing capabilities. By exploiting the parallel factor (PARAFAC) decomposition of the received signals, we derive an iterative estimation algorithm, called unitary approximate message passing (UAMP), to accurately estimate the channels between the BS and the RIS as well as the channels between the RIS and the users. To guide the selection of the system parameters, we provide the uniqueness conditions for our PARAFAC decomposition based channel estimation. To theoretically verify the efficiency of our UAMP algorithm, the Cram & eacute;r-Rao bound (CRB) of the estimation is also derived. Besides, we investigate the achievable downlink (DL) sum rate for the channel estimation obtained by the proposed algorithm by using the maximum power beam selection, the optimized phase shift matrix and the zero forcing precoding. Extensive simulation results demonstrate the excellent mean squared error (MSE) performance of our UAMP estimation algorithm. In particular, for sufficiently high UL signal-to-noise ratio, the MSE of our channel estimation reaches the CRB. Simulation results also show that the DL sum rate achieved by the estimated CSI is very close to that obtained by the perfect CSI. T

关键词： Channel estimation Millimeter wave communication Tensors Antenna arrays signal processing algorithms Estimation MISO Lenses Sparse matrices Radio frequency Reconfigurable intelligent surface lens antenna array millimeter-wave beamspace channel estimation PARAFAC Cram & eacute r-Rao bound downlink sum rate

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Robust Range Super-Resolution Imaging With Multicriteria Joint Constraints via Double Smoothed l0-Norm Under Limited Resources

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 5145-5165页

作者： Liu, Zhexian Shao, Shuai Liu, Hongwei Su, Tao Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

The radar's high-resolution range profile (HRRP) contains rich physical structural features of targets, making it of great value for target recognition and classification. However, in practical applications, the effective bandwidth of radar signals is limited due to the nonideal factors such as spectrum control, limited hardware equipment capabilities, and spectrum electromagnetic interference. Thus, it is difficult to reach the requirement of high-resolution and accurate target characterization in range profiles under limited resources, which will reduce subsequent detection, imaging, and recognition performance. To address the problem, this article proposes a robust range profile super-resolution method with multicriteria joint constraints of sparsity, low-rank, and Doppler under a multiple measurement vector model. In this technique, based on compressed sensing theory and adaptive filtering framework, a sparse reconstruction algorithm via double smoothed l(0)-norm is proposed, which constructs a continuously differentiable Gaussian class function to approximate the l(0)-norm. It combines accelerated gradient algorithm and zero attractor to optimize the multicriteria cost function and iteratively reconstruct the super-resolution range profile. The algorithm improves the super-resolution performance of range profiles, enhances the robustness against noise and complex range profile configurations, and can obtain high-precision HRRP of targets with limited radar resources. Extensive simulation and real data experiments demonstrate that it not only improves the average reconstruction accuracy of super-resolution range profile sequence, but effectively reduces the fluctuation of reconstruction errors. In addition, it does not require high azimuth resolution, and is also highly effective with a small number of sequence echoes.

关键词： Image reconstruction Superresolution Radar Accuracy Target recognition signal processing algorithms Scattering Radar imaging Vectors Doppler effect Compressed sensing (CS) double smoothed l(0) -norm (DSL0) high-resolution range profile (HRRP) limited resource multicriteria joint constraints super-resolution imaging

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Gait Symmetry Analysis With FMCW MIMO Radar

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2025年

作者： Lopez-Delgado, Ignacio E. Wang, Dingyang Fioranelli, Francesco Grajal, Jesus Univ Politecn Madrid Informat Proc & Telecommun Ctr Madrid 28040 Spain Delft Univ Technol Microwave Sensing Signals & Syst Grp MS3 NL-2628 CD Delft Netherlands

Monitoring gait symmetry reliably is crucial, as it is an early indicator of Parkinson's disease (PD). In this work, a method is presented to analyze gait asymmetries using a 24-GHz frequency-modulated continuous-wave (FMCW) multiple-input-multiple-output (MIMO) radar in a nonclinical environment. The proposed method is validated by analyzing the gait of 60 people recorded in an environment that presents multiple challenges such as multipath, gait interruption events, and different trajectories, which cause aspect angle variability. This article presents algorithms to address these challenges to extract useful gait information from the radar data and discusses the performance of the proposed system, analyzing the ratio of correct feet identifications and the accuracy of the asymmetry gait parameters. It achieved a mean absolute error of the symmetry ratio below 8% for all gait parameters. This shows that the proposed system and algorithms are robust for both clinical and in-home implementations.

关键词： Radar Foot MIMO radar Azimuth signal processing algorithms Diseases Data mining Accuracy Transmitters Receivers Frequency-modulated continuous-wave (FMCW) radar gait analysis gait symmetry multiple-input-multiple-output (MIMO) radar Parkinson's disease (PD)

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Robust Direction-of-Arrival Estimation With Outliers and Partly Calibrated Uniform Linear Array

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 4825-4834页

作者： Xiao, Wudang Li, Yingsong Yu, Canping Zhao, Luyu Pan, Mengguan de Lamare, Rodrigo C. Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Pontif Catholic Univ Rio De Janeiro PUC Rio Ctr Telecommun Res CETUC BR-22451900 Gavea Brazil Univ York Dept Elect Engn York YO10 5DD England

This article presents a three-step direction-of-arrival estimation method aimed at addressing impulsive noise and array gain-phase errors. Initially, a robustness function combining the logarithmic function with least squares is introduced to address impulsive noise. This robust function is utilized to formulate a low-rank matrix approximation problem for acquiring noise-free received data, which is solved using the alternating-direction method of multipliers. Subsequently, exploiting the obtained noise-free received data, the array gain-phase errors are estimated using the root-rank reduction estimator. Finally, based on the acquired noise-free received data and gain-phase errors, a minimization problem incorporating the logarithmic function is formulated. To efficiently solve this minimization problem, it is transformed into a weighted l(2,1)-norm minimization problem, and the fast-iterative-shrinkage-thresholding algorithm is employed. Compared to existing methods, the proposed approach demonstrates superior estimation accuracy and precision while achieving an attractive tradeoff between performance and computational complexity.

关键词： Direction-of-arrival estimation Estimation Vectors signal processing algorithms Sensor arrays Multiple signal classification Minimization Manifolds Gaussian noise Fitting Direction-of-arrival (DOA) estimation gain-phase error log-sum minimization outliers partly calibrated array sparse signal recovery

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A Time-Domain processing Framework for Airborne and Vehicle-Borne Microwave Photonic SAR With a Resolution of 0.02 m

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Lou, Yishan Xing, Mengdao Lin, Hao Ma, Penghui Sun, Guang-Cai Li, Ruoming Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Xidian Univ Acad Adv Interdisciplinary Res Xian 710071 Peoples R China Xidian Univ Hangzhou Inst Technol Hangzhou 311200 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Natl Key Lab Microwave Imaging Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 101408 Peoples R China

With the advancement of microwave photonic (MWP) synthetic aperture radar (SAR) technology, a resolution has increased to 0.02 m, and platforms have expanded from airborne to vehicle-borne. Incorporating ultrawideband (UWB), long synthetic aperture, and varied observation ranges presents two primary challenges for MWP SAR imaging: 1) the enhancement of 2-D resolution renders the imaging process more susceptible to 2-D space-variant motion errors (SVMEs) and 2) the expansion of application platforms, particularly close-range observation by vehicle-borne platforms, invalidates traditional imaging algorithms based on the far-field assumption. To address the challenges, a novel time-domain processing framework is proposed for both airborne and vehicle-borne MWP SAR systems. First, we analyze wavenumber spectrum resampling during the backprojection (BP) process, establishing a mapping relationship between phase errors in image and time domains. This allows for the estimation of trajectory deviation, enabling a rough estimation of the 2-D SVME. Subsequently, a motion compensation (MoCo) method, based on an overlapping subimage configuration combined with fast ground Cartesian BPA (GCBPA), is introduced to enable imaging. This method solves the problem that the MoCo method cannot be integrated with fast GCBPA. In the third stage, the relationship between azimuth phase error (APE) and 2-D phase error is established. Leveraging this relationship, a 2-D wavenumber domain autofocus method is developed to concurrently compensate for APE and nonsystematic range cell migration (NsRCM). Experimental validations on both airborne (0.03 m) and vehicle-borne (0.02 m) MWP SAR platforms data confirm the effectiveness and versatility of the proposed time-domain processing framework.

关键词： Synthetic aperture radar Imaging Azimuth Trajectory Time-domain analysis Radar polarimetry Support vector machines Image resolution signal processing algorithms Error analysis 2-D wavenumber domain autofocus method airborne and vehicle-borne fast ground Cartesian backprojection algorithm (GCBPA) microwave photonic (MWP) synthetic aperture radar (SAR)

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Improved Phase Generated Carrier Demodulation for Time-Division Multiplexed FBG Accelerometers

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2025年第2期43卷 942-947页

作者： Li, Tingfeng Chen, Fengyi Wang, Ruohui Qiao, Xueguang Univ Shaanxi Prov Engn Res Ctr Opt Fiber Well Logging Technol Oil & Xian 710127 Peoples R China Northwest Univ Sch Phys Xian 710127 Peoples R China

A phase generated carrier (PGC) demodulation system for fiber Bragg grating (FBG) time-domain multiplexed accelerometers array is proposed. To eliminate environmental noise effect on the Michelson interferometer(MI), a reference FBG is used. Additionally, a PGC-improved demodulation algorithm is proposed to facilitate precise demodulation of the acceleration signal. The distortion of the demodulated signal due to modulation depth fluctuations is eliminated by constructing a quadrature signal containing only the test signal. The numerical simulation results show that the output signal of the PGC-Improved demodulation algorithm is independent of the operating point of the interferometer, the carrier delay of the modulation signal, and the modulation depth. The vibration test results of the FBG accelerometer based on the flexible hinge show that the PGC-Improved algorithm achieves a gain of 15.01 dB and 57.2 dB relative to PGC-DCM and PGC-Arctan at frequencies and accelerations of 60 Hz and 1 g. The minimum total harmonic distortion value of the PGC-Improved algorithm is 0.008 at modulation depths from 1.5 rd to 3.5 rd. The noise level of the system is approximately 4 x 10(-5) rad provided that the MI is subjected to 60 Hz perturbation.

关键词： Demodulation Delays Modulation signal processing algorithms Fiber gratings Phase modulation Accelerometers Fiber-optic sensors phase generated carrier reference FBG

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An Unbalanced Optimal Transport-Based Approach for Robust Dictionary Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第6期36卷 11149-11163页

作者： Wang, Shengjia Wang, Zhiguo Zhao, Xi-Le Shen, Xiaojing Sichuan Univ Coll Math Chengdu 610064 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Math Sci Chengdu 610054 Peoples R China

Dictionary learning (DL) is a pivotal task in machine learning and signal processing, involving extracting representative features from a given dataset. However, conventional DL models are known to be highly sensitive to outliers. To circumvent this issue, we introduce a new and robust DL model based on unbalanced optimal transport (UOT). Compared to DL models based on conventional robust distances and the Wasserstein distance, our model not only captures and leverages the structural information within the data but also demonstrates strong resilience to outliers. By employing the structure of the proposed robust DL model, we develop a novel hybrid block coordinate descent (BCD) algorithm. The proposed algorithm maintains computational tractability by exploiting special block structures of the subproblems. In addition, we establish the convergence of our algorithm without the Lipschitz smooth condition. Through extensive experimentation, we validate our theoretical results and demonstrate the effectiveness of the proposed method on synthetic data, MNIST data, Olivetti faces dataset, and hyperspectral images (HSIs) datasets.

关键词： Robustness Vectors Convergence signal processing algorithms Computational modeling Training Noise Mathematical models Learning systems Feature extraction Block coordinate descent (BCD) Bregman distance robust dictionary learning (DL) unbalanced optimal transport (UOT)

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Time Scale Network: An Efficient Shallow Neural Network for Time Series Data in Biomedical Applications

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2025年第1期19卷 129-139页

作者： Meyer, Trevor Shultz, Camden Dehak, Najim Moro-Velazquez, Laureano Irazoqui, Pedro Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA

Time series data is often composed of information at multiple time scales, particularly in biomedical data. While numerous deep learning strategies exist to capture this information, many make networks larger, require more data, are more demanding to compute, and are difficult to interpret. This limits their usefulness in real-world settings facing even modest computational or data constraints and can further complicate their translation into real-time processing or edge device applicaitons. We present a minimal, computationally efficient Time Scale Network combining the translation and dilation sequence used in discrete wavelet transforms with traditional convolutional neural networks and back-propagation. The network simultaneously learns features at many time scales for sequence classification with significantly reduced parameters and operations. We demonstrate advantages in Atrial Dysfunction detection including: superior accuracy-per-parameter and accuracy-per-operation, fast training and inference speeds, and visualization and interpretation of learned patterns in atrial dysfunction detection on ECG signals. We also demonstrate impressive performance in seizure prediction using EEG signals, where our network isolated a few time scales that could be strategically selected to achieve 90.9% accuracy using only 1,133 active parameters and consistently converged on pulsatile waveform shapes. This method does not rest on any constraints or assumptions regarding signal content and could be leveraged in any area of time series analysis dealing with signals containing features at many time scales.

关键词： Transforms signal processing algorithms Time-frequency analysis Feature extraction Accuracy signal processing Computer architecture Efficiency wavelet shallow NN biomedical

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Robust Multichannel Decorrelation via Tensor Einstein Product

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 275-291页

作者： Chang, Shih-Yu Wu, Hsiao-Chun Liu, Guannan 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 Chungli 32003 Taiwan

Decorrelation of multichannel signals has played a crucial preprocessing role (in prewhitening and orthogonalization) for many signal processing applications. Classical decorrelation techniques can only be applied for signal vectors. Nonetheless, many emerging big-data and sensor-network applications involve signal tensors (signal samples required to be arranged in a tensor form of arbitrary orders). Meanwhile, the existing tensor-decorrelation methods have serious limitations. First, the correlation-tensors have to be of certain particular orders. Second, the unrealistic assumption of the specific signal-tensor form, namely the canonical polyadic (CP) form, is made. Third, the correlation-tensor has to be full-rank or an extra preprocessor based on principal component analysis is required for any non-full-rank correlation tensor. To remove the aforementioned impractical limitations, we propose a novel robust approach for high-dimensional multichannel decorrelation, which can accommodate signal tensors of arbitrary orders, forms, and ranks without any need of extra preprocessor. In this work, we introduce two new tensor-decorrelation algorithms. Our first new algorithm is designed to tackle full-rank correlation-tensors and our second new algorithm is designed to tackle non-full-rank correlation-tensors. Meanwhile, we also propose a new parallel-computing paradigm to accelerate our proposed new tensor-decorrelation algorithms. To demonstrate the applicability of our proposed new scheme, we also apply our proposed new tensor-decorrelation approach to pre-whiten the tensor signals and analyze the corresponding convergence-speed and misadjustment performances of the tensor least-mean-squares (TLMS) filter. Finally, we assess the computational- and memory-complexities of our proposed new algorithms by simulations over both artificial and real data. Simulation results show that our proposed new multichannel-decorrelation algorithms outperform the existing tensor-decorrel

关键词： Tensors Accuracy Computational modeling Simulation signal processing algorithms Estimation Filtering algorithms Vectors Decorrelation Convergence Tensor signal processing Einstein product multichannel decorrelation correlation tensor whitening eigenspread

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Practical Compact Deep Compressed Sensing

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第3期47卷 1610-1626页

作者： Chen, Bin Zhang, Jian Peking Univ Sch Elect & Comp Engn Shenzhen 518055 Peoples R China

Recent years have witnessed the success of deep networks in compressed sensing (CS), which allows for a significant reduction in sampling cost and has gained growing attention since its inception. In this paper, we propose a new practical and compact network dubbed PCNet for general image CS. Specifically, in PCNet, a novel collaborative sampling operator is designed, which consists of a deep conditional filtering step and a dual-branch fast sampling step. The former learns an implicit representation of a linear transformation matrix into a few convolutions and first performs adaptive local filtering on the input image, while the latter then uses a discrete cosine transform and a scrambled block- diagonal Gaussian matrix to generate under-sampled measurements. Our PCNet is equipped with an enhanced proximal gradient descent algorithm-unrolled network for reconstruction. It offers flexibility, interpretability, and strong recovery performance for arbitrary sampling rates once trained. Additionally, we provide a deployment-oriented extraction scheme for single-pixel CS imaging systems, which allows for the convenient conversion of any linear sampling operator to its matrix form to be loaded onto hardware like digital micro-mirror devices. Extensive experiments on natural image CS, quantized CS, and self-supervised CS demonstrate the superior reconstruction accuracy and generalization ability of PCNet compared to existing state-of-the-art methods, particularly for high-resolution images.

关键词： Artificial neural networks Image reconstruction Imaging Matrix converters Hardware Filtering Training signal processing algorithms Optimization Micromirrors Compressed sensing sampling matrix single-pixel imaging algorithm unrolling and structural reparameterization

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