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Atomic Norm Regularized Tensor Completion for Angle Estimation in Bistatic MIMO Radar With Sensor Failures

IEEE SENSORS JOURNAL 2025年第2期25卷 2934-2948页

作者： Chen, Jinli Song, Yang Zhang, Jincan Yao, Changhua Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China

Faulty array sensors pose a significant challenge in accurately determining the target angles in bistatic multiple-input multiple-output (MIMO) radar systems. To remedy this, we propose a novel method utilizing atomic norm regularized tensor completion for joint estimates of the direction of departure (DOD) and direction of arrival (DOA). We first develop a third-order covariance tensor model for the sensor array data in bistatic MIMO radar and cast the reconstruction of missing entries caused by faulty array sensors as a low-rank tensor completion (LRTC) problem with structurally missing entries. By leveraging the Vandermonde structure inherent in the factor matrices from the CANDECOMP/PARAFAC (CP) decomposition of the covariance tensor, we impose atomic norm constraints on the factor vectors to formulate an atomic norm regularized tensor completion model. Then, an efficient optimization algorithm based on the alternating direction method of multipliers (ADMM) is developed to solve the proposed model. Moreover, to enforce the Vandermonde structure of the factor matrices, we integrate difference coarray processing to rectify the factor matrices during iterations. Finally, joint estimates of DOD and DOA are obtained by applying the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm to the Toeplitz matrices derived from the optimized vectors. Extensive simulations demonstrate that the proposed algorithm achieves superior angle estimation accuracy and computational efficiency compared to state-of-the-art algorithms when handling array sensor failures.

关键词： Tensors Sensors Sensor arrays MIMO radar Covariance matrices Estimation Vectors Matrix decomposition Array signal processing signal processing algorithms Angle estimation atomic norm differential coarray multiple-input multiple-output (MIMO) radar sensor failures tensor completion

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Deep-Unfolded Massive Grant-Free Transmission in Cell-Free Wireless Communication Systems

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 1094-1109页

作者： Sun, Gangle Cao, Mengyao Wang, Wenjin Xu, Wei Studer, Christoph Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211100 Peoples R China Swiss Fed Inst Technol Dept Informat Technol & Elect Engn CH-8092 Zurich Switzerland

Grant-free transmission and cell-free communication are vital in improving coverage and quality-of-service for massive machine-type communication. This paper proposes a novel framework of joint active user detection, channel estimation, and data detection (JACD) for massive grant-free transmission in cell-free wireless communication systems. We formulate JACD as an optimization problem and solve it approximately using forward-backward splitting. To deal with the discrete symbol constraint, we relax the discrete constellation to its convex hull and propose two approaches that promote solutions from the constellation set. To reduce complexity, we replace costly computations with approximate shrinkage operations and approximate posterior mean estimator computations. To improve active user detection (AUD) performance, we introduce a soft-output AUD module that considers both the data estimates and channel conditions. To jointly optimize all algorithm hyper-parameters and to improve JACD performance, we further deploy deep unfolding together with a momentum strategy, resulting in two algorithms called DU-ABC and DU-POEM. Finally, we demonstrate the efficacy of the proposed JACD algorithms via extensive system simulations.

关键词： signal processing algorithms Channel estimation Wireless communication Approximation algorithms Vectors Optimization Machine learning algorithms Training Sparse matrices Decoding Active user detection cell-free communication channel estimation data detection deep unfolding grant-free transmission massive machine-type communication

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A Power-and-Area-Efficient Channel-Interleaved Neural signal Processor for Wireless Brain-Computer Interfaces With Unsupervised Spike Sorting

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2025年第1期19卷 108-119页

作者： Hu, Zichen Zhou, Zhining Lyu, Hongming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai 200050 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China ShanghaiTech Univ State Key Lab Adv Med Mat & Devices Shanghai 201210 Peoples R China Shanghai Engn Res Ctr Energy Efficient & Custom AI Shanghai 201210 Peoples R China

Next generation of wireless brain-computer-interface (BCI) devices require dedicated neural signal processors (NSPs) to extract key neurological information while operating within given power consumption and transmission bandwidth limits. Spike detection and clustering are important signal processing steps in neuroscience research and clinical applications. Computational-friendly spike detection and feature extraction algorithms are first systematically evaluated in this work. The nonlinear energy operator (NEO) and the first-and-second-derivative (FSDE) together with the 'perturbed' K-mean clustering achieve the highest accuracy performance. An NSP ASIC is implemented in a channel-interleaved architecture and the folding ratio of 16 leads to the minimum power-and-area product. As the result, the NSP consumes 2-mu W power consumption and occupies 0.0057 mm(2) for each channel in a 65-nm CMOS technology. The proposed system achieves the unsupervised spike classification accuracy of 92% and a data-rate reduction of 98.3%, showing the promise for realizing high-channel-count wireless BCIs.

关键词： Feature extraction signal processing algorithms Wireless communication Clustering algorithms Bandwidth Noise level Hardware Neural signal processing spike detection feature extraction unsupervised clustering spike sorting ASIC channel-interleaving CMOS low-power hardware-efficiency

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Enhanced Knowledge Tracing With Learnable Filter

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年第1期12卷 198-209页

作者： Qian, Fulan Hu, Yetong Li, Guangyao Chen, Jie Wang, Shijin Zhao, Shu Anhui Univ Artificial Intelligence Inst Hefei 230601 Peoples R China Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China IFLYTEK AI Res Hefei 230088 Peoples R China

The primary objective of knowledge tracing (KT) is to evaluate students' understanding and mastery of knowledge through their responses to exercises, which aids in predicting their future performance. Deep neural networks have been widely applied in the area of knowledge tracing and have demonstrated encouraging results. Nevertheless, in real-world scenarios, there is a substantial amount of noise in students' response records. These noises may amplify the inherent risk of overfitting in deep neural networks, leading to a decrease in model performance. To address these issues, we introduce a new model called filter knowledge tracing (FKT). This innovative model incorporates a learnable filter into KT to filter out noise information from students' exercise sequences. We redefine the input paradigm of the data, using learnable filters to perform filtering operations in its frequency domain representation space, effectively removing noise. Additionally, an attention module has been introduced in the FKT model to evaluate the impact of students' historical interactions on their current knowledge state. To validate our model, we conduct extensive experiments utilizing four publicly available datasets. The results demonstrate that FKT outperforms existing benchmarks, particularly on larger datasets, signifying an improvement in KT performance while effectively reducing the risk of overfitting.

关键词： Data models Noise Noise reduction signal processing algorithms Knowledge engineering Prediction algorithms Noise measurement Heuristic algorithms Frequency-domain analysis Filtering algorithms Attention mechanism denoising filtering algorithm knowledge tracing (KT)

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Simplified Fast List PAC Decoder for Broadcasting Services in 6G: Algorithm and Implementation

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IEEE TRANSACTIONS ON BROADCASTING 2025年

作者： Dai, Jingxin Yin, Hang Lv, Yansong Wang, Yuhuan Xu, Yin Lv, Rui Commun Univ China State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai 200240 Peoples R China

In the 6G network, integrating broadcasting and mobile networks will significantly improve the transmission capability. Considering the excellent error-correction performance, polarized-adjusted convolutional (PAC) codes are promising for ensuring reliable data transmission in 6G broadcasting services. However, the inherent high decoding latency of PAC codes poses challenges for seamless switching between broadcasting and mobile services. In this paper, we propose a simplified fast list (SFL) PAC decoder, which jointly exploits the node thresholds and adaptive path-pruning technology to reduce the decoding latency while maintaining high reliability. Firstly, we present a novel path expansion rule based on the node thresholds to avoid unnecessary computations. Secondly, the introduction of the adaptive path-pruning technology efficiently reduces the number of sorting operations. Moreover, we implement the proposed decoder on general purpose processors (GPPs) by software. Simulation results show that the proposed SFL decoding algorithm significantly reduces the decoding latency by up to 75.18% compared to the state-of-the-art (SOTA) work with no noticeable degradation in error-correction performance. Software implementation of the proposed decoder achieves an 18.80% improvement in throughput performance over the SOTA PAC software decoder.

关键词： Decoding Picture archiving and communication systems Software Codes 6G mobile communication Broadcasting Software algorithms Throughput Polar codes signal processing algorithms PAC codes broadcasting decoding latency threshold decoder software

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Super-Resolution Range-Velocity Estimate of Multiple Targets for OFDM-Based 5G Radar Based on Unitary Parallel Factor Method

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第3期74卷 4048-4063页

作者： Cao, Chenghu Huang, Haisheng Zhao, Yongbo Xian Univ Posts & Telecommun Xian 710121 Peoples R China Yanan Univ Xian Innovat Coll Xian 710100 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Xidian Univ Informat Sensing & Understanding Xian 710071 Peoples R China

In this paper, we consider the problem of joint range-velocity estimate of multiple targets in orthogonal frequency division multiplex (OFDM) transmit waveform-based 5G radar. A unitary parallel factor (PARAFAC) algorithm is proposed to achieve super-resolution estimate and outstanding performance, using forward-backward averaging scheme. The forward-backward averaging scheme is adopted to construct real-valued tensor signal model instead of the complex-valued one, yielding the better accuracy at modest complexity. The proposed unitary PARAFAC algorithm is performed by decomposing the real-valued tensor without signal subspace estimate. Due to the inherent smoothing processing of the proposed unitary PARAFAC algorithm, it can effectively deal with high correlated target. Additionally, the proposed unitary PARAFAC algorithm can automatically obtain pair parameters including range and velocity of the same target without additional pair-matching operation. More importantly the regularized alternative least squares (RALS) algorithm is used to improve the decomposition performance of the real-valued tensor while maintaining iteration stability. The numerical results are presented to demonstrate the superior performance, especially for high correlated and closely spaced targets in low-SNR scenario.

关键词： Radar signal processing algorithms Tensors 5G mobile communication OFDM Sensors Symbols Accuracy Radar applications Radar signal processing Forward-backward averaging scheme OFDM-based 5G radar real-valued tensor signal model regularized alterative least squares unitary parallel factor

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Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Neural Responses to Natural Stimuli

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2025年第2期29卷 970-983页

作者： Geirnaert, Simon Yao, Yuanyuan Francart, Tom Bertrand, Alexander Katholieke Univ Leuven STADIUS Ctr Dynam Syst Signal Proc & Data Analyt Dept Elect Engn ESAT B-3000 Leuven Belgium Katholieke Univ Leuven Inst AI Leuven AI B-3001 Leuven Belgium Katholieke Univ Leuven Dept Neurosci Res Grp ExpORL B-3000 Leuven Belgium

Various new brain-computer interface technologies or neuroscience applications require decoding stimulus-following neural responses to natural stimuli such as speech and video from, e.g., electroencephalography (EEG) signals. In this context, generalized canonical correlation analysis (GCCA) is often used as a group analysis technique, which allows the extraction of correlated signal components from the neural activity of multiple subjects attending to the same stimulus. GCCA can be used to improve the signal-to-noise ratio of the stimulus-following neural responses relative to all other irrelevant (non-)neural activity, or to quantify the correlated neural activity across multiple subjects in a group-wise coherence metric. However, the traditional GCCA technique is stimulus-unaware: no information about the stimulus is used to estimate the correlated components from the neural data of several subjects. Therefore, the GCCA technique might fail to extract relevant correlated signal components in practical situations where the amount of information is limited, for example, because of a limited amount of training data or group size. This motivates a new stimulus-informed GCCA (SI-GCCA) framework that allows taking the stimulus into account to extract the correlated components. We show that SI-GCCA outperforms GCCA in various practical settings, for both auditory and visual stimuli. Moreover, we showcase how SI-GCCA can be used to steer the estimation of the components towards the stimulus. As such, SI-GCCA substantially improves upon GCCA for various purposes, ranging from preprocessing to quantifying attention.

关键词： Electroencephalography Decoding Correlation signal to noise ratio signal processing algorithms Synchronization Neuromarketing Generalized canonical correlation analysis correlated component analysis electroencephalography stimulus-following neural response

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Multiparameter Joint GNSS Spoofing Detection Based on TSVAE

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 3373-3386页

作者： Sun, Peng Xiong, Hailiang Shah, Danyal Hussain Liu, Yuncheng Zhou, Bin Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China Shandong Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China

Spoofing attacks pose a significant threat to the global navigation satellite system (GNSS). However, existing techniques encounter challenges in accurately identifying spoofing signals, particularly of unknown attacks in complex and changing scenarios. In this article, we propose a multiparameter GNSS joint spoofing detection method based on temporal-spatio variational auto-encoders (TSVAE). The proposed model has demonstrated high precision in detecting unknown spoofing interference by utilizing a clean signal as a training dataset. Additionally, we have refined the conventional signal quality monitoring (SQM) metrics. The improved metrics display more distinct distributional characteristics and are better suited for adapting to unsupervised learning algorithms. We further test the proposed algorithm under multipath and electromagnetic interference to evaluate its anti-interference capability. The test results on the TEXBAT and OAKBAT datasets demonstrate that the proposed algorithm exhibits exceptional detection performance and robustness.

关键词： Interference Codes Global navigation satellite system Correlation Receivers signal processing algorithms Correlators Monitoring Aerospace and electronic systems Satellites Carrier to noise ratio global navigation satellite system (GNSS) neural network signal quality monitoring (SQM) spoofing interference

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Scalable Multisubject Vital Sign Monitoring With mmWave FMCW Radar and FPGA Prototyping

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IEEE SENSORS JOURNAL 2025年第2期25卷 3571-3583页

作者： Benny, Jewel Moudhgalya, Narahari N. Khan, Mujeev Meena, Hemant Kumar Wajid, Mohd Srivastava, Abhishek IIIT Hyderabad Ctr VLSI & Embedded Syst Technol CVEST Hyderabad 500032 India Aligarh Muslim Univ Dept Elect Engn Aligarh 202001 Uttar Pradesh India Malaviya Natl Inst Technol MNIT Jaipur Dept Elect Engn Jaipur 302017 Rajasthan India

In this work, we introduce an innovative approach to estimate the vital signs of multiple human subjects simultaneously in a noncontact way using a frequency-modulated continuous-wave (FMCW) radar-based system. Traditional vital sign monitoring methods often face significant limitations, including subject discomfort with wearable devices, challenges in calibration, and the risk of infection transmission through contact measurement devices. To address these issues, this research is motivated by the need for versatile, noncontact vital monitoring solutions applicable in various critical scenarios. This work also explores the challenges of extending this capability to an arbitrary number of subjects, including hardware and theoretical limitations. Supported by rigorous experimental results and discussions, the article illustrates the system's potential to redefine vital sign monitoring. A field-programmable gate array (FPGA)-based implementation is also presented as proof of concept for a hardware-based and portable solution, improving upon previous works by offering 2.7x faster execution and 18.4% less look-up table (LUT) utilization, as well as providing over 7400x acceleration compared to its software counterpart.

关键词： Azimuth Ultra wideband radar Frequency measurement Doppler radar signal processing algorithms Monitoring Biomedical measurement Accuracy Sensors Radar measurements 77 GHz breath-rate estimation field-programmable gate array (FPGA) frequency-modulated continuous wave (FMCW) healthcare heart-rate estimation mmWave radar multisubject noncontact

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Efficient Fault Localization in Smart Grid Through Analysis of the Wave Matrix Image Using Convolutional Neural Networks

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IEEE ACCESS 2025年 13卷 53407-53419页

作者： Carlos Filho, Jose Abreu, Francisco C. M. Silva, Mauricio Borges, Fabbio Rabelo, Ricardo A. L. Fed Inst Educ Sci & Technol Piaui Dept Ind Secur & Cultural Prod BR-64000040 Teresina Brazil Fed Univ Piaui UFPI Dept Comp Sci BR-64049550 Teresina Brazil Sch Mkt Recife SORECH BR-51030065 Recife Brazil State Univ Piaui UESPI Dept Comp Sci BR-64001030 Teresina Brazil

This article presents a methodology for fault localization in electric power distribution systems through the analysis of wave matrix image using Convolutional Neural Networks (CNN). Ensuring a continuous and high-quality supply of electric power is crucial for the efficient operation of a Power System. However, the extensive coverage of the Electric System makes it susceptible to various disturbances caused by factors such as adverse weather conditions, equipment failures, presence of animals in the networks, and human errors. These disturbances can result in faults, characterized as short-circuits or abnormal currents between conductors or to the ground. With approximately 80% of power supply interruptions attributed to Distribution System faults, the need for effective fault localization methods is evident. By extracting voltage signal characteristics at measurement points during disturbances and transforming them into a panoramic representation of the system in the form of an image, the panoramic image generation, derived from simulated short-circuit values at each system bus, provides a comprehensive visualization of the network, enabling precise fault localization. The results demonstrate the accuracy of the CNN method in fault localization. This approach offers scalability, efficient transformation into images, and precise fault localization in electric power distribution systems.

关键词： Location awareness Convolutional neural networks Fault location Feature extraction Voltage measurement Power systems Smart grids Deep learning Accuracy signal processing algorithms Fault localization power systems convolutional neural networks panoramic image data analysis

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