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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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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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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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Practical Implementation Challenges in MIMO Beamforming and Its Applications

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IEEE MICROWAVE MAGAZINE 2025年第4期26卷 59-75页

作者： Dusari, Nageswara Rao Rawat, Meenakshi Indian Inst Technol Roorkee Dept Elect & Commun Engn Roorkee 247667 India

Adaptive beamforming is a crucial signal processing technique used in 5G and beyond to mitigate path losses and improve energy and spectral efficiency by transmitting and receiving signals directionally. The MIMO beamforming systems contain multiple power amplifiers (PAs), which consume substantial power and require efficient linearization to enhance energy efficiency and reduce nonlinear distortions. However, implementing beamforming and PA linearization in real time introduces significant challenges, including calibration, synchronization, and hardware constraints. This article presents the practical challenges of implementing beamforming and digital predistortion (DPD) algorithms in hardware platforms and measurement processes. This article also discusses advanced techniques for linearizing PAs and overcoming measurement-related issues in beamforming systems.

关键词： Array signal processing Transmitters 5G mobile communication Spectral efficiency signal processing algorithms Hardware Real-time systems Calibration Synchronization Distortion measurement

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Power-Enhanced GNSS Interference Mitigation With Sensed Equivalent Bandwidth Based on ASF Algorithm

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IEEE SENSORS JOURNAL 2025年第2期25卷 2886-2896页

作者： Song, Jie Lu, Zukun Xiao, Wei Li, Chenglong Wang, Yuqing Sun, Guangfu Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China Key Lab Satellite Nav Technol Changsha 410073 Peoples R China Hunan Inst Adv Technol Yueyang 414015 Peoples R China

Power-enhancement is a primary focus in the global navigation satellite system (GNSS) to improve its anti-interference capabilities. However, interference issues cannot be fully resolved due to the limited increase in signal power. Additionally, traditional interference mitigation methods often misclassify signals as interference and suppress them, leading to significant GNSS signal loss. This article proposes an adaptive sparse filtering (ASF) method based on sensed equivalent bandwidth, enabling interference mitigation for GNSS power-enhancement without additional signal loss. By analyzing the spectral relationship between interference and signal bandwidth, the equivalent bandwidth that ignores the strong signal recognition is derived, and the sparse sampling model is developed to reconstruct the interference mitigation filter. Simulations and practical tests verify the effectiveness of this interference mitigation method, which is broadly applicable to both conventional and power-enhanced GNSS signals. It improves anti-interference performance by 3-5 dB without introducing additional computational complexity associated with spectral awareness. This improvement in signal quality also results in the 19% increase in positioning accuracy.

关键词： Interference Global navigation satellite system Filtering algorithms Prevention and mitigation signal processing algorithms Receivers Filtering theory Satellites Information filters Vectors Adaptive sparse filtering (ASF) global navigation satellite system (GNSS) power-enhancement high anti-interference performance interference mitigation low-cost sensed equivalent bandwidth

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Modified Time-Domain Backprojection Algorithm for SAR Frequency-Domain Autofocus

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Liu, Yanqi Fan, Jixia Tao, Manyi Shi, Tianyue Mao, Xinhua Nanjing Univ Aeronaut & Astronaut Key Lab Radar Imaging & Microwave Photon Minist Educ Nanjing 211106 Peoples R China Shanghai Acad Spaceflight Technol Shanghai 201109 Peoples R China Shanghai Inst Satellite Engn Shanghai 201109 Peoples R China Nanjing Univ Aeronaut & Astronaut Shenzhen Res Inst Shenzhen 518063 Peoples R China

The backprojection (BP) algorithm is a precise time-domain technique in synthetic aperture radar (SAR) imaging, known for its robustness in scenarios where frequency-domain algorithms struggle, such as nonlinear flight paths, high-resolution demands, or large imaging swaths. However, the unknown spectral structures of BP images pose significant challenges for frequency-domain autofocus algorithms, including phase gradient autofocus (PGA). This article introduces a novel interpretation of the BP algorithm, and then compares the image spectra between BP and the polar format algorithm (PFA). Our findings indicate that BP image's spectrum suffers from severe range ambiguity and spectral misalignment, which is the key reason why BP images cannot be directly applied to PGA. To address these issues, we first improve the postprocessing method, and then propose a modified BP algorithm. Both the approaches reconstruct the signal spectrum, thereby facilitating the direct application of PGA. Compared with the postprocessing method, the proposed algorithm is more efficient. Simulation and real data experiments validate the feasibility and efficacy of the proposed method, demonstrating the proposed algorithm's suitability for autofocusing in the frequency domain.

关键词： Radar imaging Electronics packaging Frequency-domain analysis Imaging History signal processing algorithms Fourier transforms Radar polarimetry Azimuth Synthetic aperture radar Backprojection (BP) frequency-domain autofocus spectrum analysis synthetic aperture radar (SAR)

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A Digital LLRF System Based on Intrapulse Phase Feedback for LINAC in Hefei Advanced Light Facility

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2025年第3期72卷 483-490页

作者： Li, Zhenyan Qin, Jiajun Zhao, Lei Hou, Kexi Du, Baiting Ma, Yuelei Univ Sci & Technol China State Key Lab Particle Detect & Elect Hefei 230026 Peoples R China Univ Sci & Technol China Dept Modern Phys Hefei 230026 Peoples R China Univ Sci & Technol China Natl Synchrotron Radiat Lab Hefei 230026 Peoples R China

The linear accelerator system (LINAC) in Hefei advanced light facility (HALF) under construction will accelerate the beam to 2.2 GeV to reach the required luminosity. To achieve stable amplitude and phase control of the radio frequency (RF) field inside the acceleration tubes, low-level RF (LLRF) systems should be adopted. As the solid-state amplifier (SSA) and klystron operate in the saturated state, the system requires amplitude open-loop control and stable phase close-loop control during large amplitude disturbance, proposing a demand for the online phase stability to be better than 0.2 degrees rms with a klystron. In this article, we introduce a digital LLRF system designed for the LINAC of HALF. The LLRF system aims to achieve a fast intrapulse phase feedback control independent of the amplitude control, which is conducive to high phase-control stability. The system obtains digital phase data directly from a phase discriminator (PD) and an analog-to-digital converter (ADC). Besides, the independent output phase control is achieved through an analog time delay chip (ATD). As a result, the digital signal processing (DSP) algorithm is simplified, which is beneficial for achieving low closed-loop latency. Finally, we present the preliminary test results of the LLRF system, proving that the system could achieve fast and independent phase feedback control in one pulse duration.

关键词： Radio frequency Linear particle accelerator Phase control signal processing algorithms Phase modulation Phase detection Klystrons Hefei advanced light facility (HALF) intrapulse phase feedback linear accelerator system (LINAC) low-level radio frequency (LLRF) system

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Enhancing Nonintrusive Load Monitoring With Targeted Adaptive Networks

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zhao, Maojiang Chen, Song Xiong, Zuqiang Bai, Zhemin Yang, Yu Xiamen Univ Dept Elect Sci Xiamen 361005 Peoples R China Huawei Technol Co Ltd Hangzhou 310012 Peoples R China

Nonintrusive load monitoring (NILM) technology is a cost-effective method that infers the operating status and working conditions of each system appliance by disaggregating the overall electricity load recorded by the electric meter. This technology has numerous potential applications in the smart grid, including energy management, demand response, and fault detection. However, its effectiveness highly depends on the development of accurate disaggregation algorithms. Recently, deep learning (DL) has significantly enhanced the accuracy and computational efficiency of NILM. Despite these advancements, the generalization ability of most DL algorithms is limited due to the scarcity of training data. To address this limitation, we propose a new method named targeted adaptive network for NILM (TAN-NILM). This method features a main disaggregation network and an auxiliary network, trained using a multitask learning strategy. The auxiliary network extracts and provides feature information on individual appliances, assisting the main network in targeting specific appliances and estimating their loads. The experimental results demonstrate that TAN-NILM exhibits superior disaggregation accuracy and generalization ability compared to state-of-the-art NILM algorithms.

关键词： signal processing algorithms Home appliances Feature extraction Training Accuracy Time-domain analysis Filtering Multitasking Load monitoring Adaptation models Auxiliary network mix source separation multitask learning nonintrusive load monitoring (NILM) self-attention

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Weighted Average Consensus algorithms in Distributed and Federated Learning

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第2期12卷 1369-1382页

作者： Tedeschini, Bernardo Camajori Savazzi, Stefano Nicoli, Monica Politecn Milan I-20133 Milan Italy Consiglio Nazl Ric CNR I-20133 Milan Italy

The exponential growth of the Internet of Things (IoT) has created an essential demand for Distributed Machine Learning (DML) systems. In this context, Federated Learning (FL) allows IoT devices to collaboratively train models while maintaining data ownership and privacy. Despite the evident advantages, FL faces practical challenges such as client selection and adaptation to heterogeneous data distributions. Recently, consensus-driven algorithms have been proposed to enable efficient and scalable FL without a central coordinating entity. Weighted Average Consensus (WAC) tools, primarily used in distributed signal processing, fail to address FL-specific challenges. The paper proposes a new family of server-less FL algorithms optimized to exploit WAC techniques. In particular, we propose an evolution of the centralized Federated Adaptive Weighting (FedAdp) method and present three distinct WAC schemes specifically designed for non-Independent and Identical Distributed (IID) data. Each scheme has a unique aggregation part that optimizes the weights of the clients' local models. The performances are evaluated in a real-world IoT system, analyzing their convergence properties in the context of heterogeneous client populations. Results show that the proposed algorithms outperform vanilla consensus FL up to 56% of accuracy and they are resilient to both label and sample data skewness.

关键词： Data models Heuristic algorithms Internet of Things Adaptation models Convergence signal processing algorithms Vehicle dynamics Optimization Distributed databases Training FL over networks weighted average consensus non-independent and identical distributed edge computing

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