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Clutter Suppression for STAP-Based Radar Using Synthesized Subarray Beampattern

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 4507-4525页

作者： Wan, Fuhai Xu, Jingwei Xu, Yanhong Lan, Lan Wang, Weiwei Liao, Guisheng Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Xian Univ Sci & Technol Sch Commun & Informat Engn Xian 710054 Peoples R China China Acad Space Technol Xian 710100 Peoples R China

The space-time-coupled grating lobe clutter (GLC) usually exists and is difficult to handle for subarray-based phased array airborne radar, which would cause serious performance degradation in clutter suppression. To alleviate this problem, this article presents a data-independent subarray beampattern synthesis approach for the space-time adaptive processing (STAP) based radar. In particular, the GLC spectrum is first analyzed in detail, based on which the receive overlapped subarray beampattern is designed with certain nulls located at the grating lobe regions to prefilter of GLC. During the GLC suppression, the subarray beampattern synthesis algorithm is devised based on the criterion of maximizing array gain, where phase ambiguity is incorporated into the optimization model to precisely adjust the power responses of the receive subarray beampattern. In this way, the GLC can be prefiltered in subarray and its coupling diffusion characteristic in the space-time domain can be prevented, thus improving the clutter suppression and moving target detection capabilities. In what follows, the subarray-level STAP filter is implemented for the residual space-time coupling mainlobe and sidelobe clutter suppression. Numerical results demonstrate that the proposed subarray beampattern synthesis method can remarkably enhance the output signal-to-clutter-plus-noise ratio loss performance in GLC regions.

关键词： Clutter Gratings Vectors Doppler effect Radar clutter Spaceborne radar Optimization signal processing algorithms Doppler radar Covariance matrices Grating lobe clutter (GLC) suppression overlapped subarray prefiltering space-time adaptive processing (STAP) subarray beampattern synthesis

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Detecting Anxiety via Machine Learning algorithms: A Literature Review

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Tayarani-N., M. -H. Shahid, Shamim Ibne Univ Hertfordshire Sch Phys Engn & Comp Sci HATFIELD Hatfield AL10 9AB England

Recent machine learning (ML) advances have opened up new possibilities for addressing various challenges. Given their ability to tackle complex problems, the use of ML algorithms in diagnosing mental health disorders has seen substantial growth in both the number and scope of studies. Anxiety, a major health concern in today's world, affects a significant portion of the population. Individuals with anxiety often exhibit distinct characteristics compared to those without the disorder. These differences can be observed in their outward appearance-such as voice, facial expressions, gestures, and movements-and in less visible factors like heart rate, blood test results, and brain imaging data. In this context, numerous studies have utilized ML algorithms to extract a diverse range of features from individuals with anxiety, aiming to build predictive models capable of accurately identifying those affected by the disorder. This paper performs a comprehensive literature review on the state-of-the-art studies that employ machine learning algorithms to identify anxiety. This paper aims to cover a wide range of studies and categorize them based on their methodologies and data types used.

关键词： Anxiety disorders Machine learning algorithms Mental health Prediction algorithms Feature extraction Depression Accuracy Systematic literature review Sleep signal processing algorithms Anxiety disorder machine learning artificial intelligence mental disorder social signal processing affective computing

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Joint Inter-Symbol Interference and I/Q Imbalance Cancellation in FTN Systems

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第4期24卷 2680-2692页

作者： Tong, Mingfei Huang, Xiaojing Zhang, J. Andrew Univ Technol Sydney Global Big Data Technol Ctr Ultimo NSW 2007 Australia

Current research on faster-than-Nyquist (FTN) systems mainly focuses on baseband digital signal processing without considering the impact of I/Q imbalance (IQI) caused by hardware impairments in the signal chain. To address this problem, this paper considers frequency-dependent IQI and applies the frame-based decision-directed successive interference cancellation (DDSIC) algorithm after minimum mean square error (MMSE) equalization to jointly mitigate inter-symbol interference (ISI) and IQI. We introduce extended-dimension signal models, which use both original and image signals to describe the impact of IQI. Based on the models, a two-stage iterative DDSIC algorithm is then proposed, achieving effective interference cancellation. Furthermore, the theoretical bit error rate (BER) for each iteration of DDSIC and the BER lower bound of the proposed system are derived. Simulation results demonstrate the superiority of DDSIC over some existing algorithms under both additive white Gaussian noise (AWGN) and multipath fading channels. These results also validate the derived theoretical BER expressions and the robustness of our scheme under various ISI and IQI scenarios, respectively.

关键词： Symbols Wireless communication Bit error rate signal processing algorithms Receivers Accuracy OFDM Iterative algorithms System performance Prevention and mitigation FTN signaling I/Q imbalance MMSE equalization ISI cancellation IQI cancellation

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Joint HW/SW signal Co-Conditioning Strategy for Unobtrusive Single-Arm Cardiac Surveillance

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Huang, Linfei Chen, Jiarong Zhang, Xianbin Li, Zhen Xu, Lin Millham, Richard Bian, Gui-Bin Wu, Wanqing Sun Yat Sen Univ Sch Biomed Engn Shenzhen 518107 Peoples R China Shanghai Jiao Tong Univ Dept Micro Nano Elect Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai 200240 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Guangdong Pharmaceut Univ Clin Med Sch 1 Guangzhou 510006 Peoples R China Durban Univ Technol Dept Informat Technol ZA-4001 Durban Kwazulu Natal South Africa Sun Yat Sen Univ Sch Biomed Engn Guangdong Prov Key Lab Sensor Technol & Biomed Ins Guangzhou 510275 Guangdong Peoples R China

The electrocardiogram (ECG) is a crucial tool for cardiac health monitoring, but existing ECG monitoring systems often lack effective signal conditioning coordination between hardware (HW) and software (SW), resulting in poor signal quality and monitoring effectiveness. To address this issue, this study presents a novel single-arm ECG monitoring system based on the Internet of Things (IoT) that comprehensively considers both hardware and software aspects. The system mainly consists of an ECG measurement device and a signal reconstruction algorithm. For the device, impedance matching circuits and preamplifier circuits were specially designed to measure the weak single-arm ECG signal. Additionally, to improve wearing comfort, a metal dry electrode and an armband were developed. On the software side, an ECG signal reconstruction algorithm based on a diffusion model is proposed, and the reverse process of the diffusion model and the noise prediction model are improved according to the characteristics of the ECG signal. The feasibility of the system was verified through monitoring experiments conducted in real environments on healthy subjects and heart disease patients. The experimental results show that the proposed measurement device is capable of long-term ECG monitoring and that the signal reconstruction algorithm effectively removes various real-world noises. The R-wave detection precision, sensitivity, and F1-score of the reconstructed signal improved from 75.90% to 85.87%, 69.16% to 83.40%, and 72.37% to 84.62%, respectively. This demonstrates that the system provides a new solution for efficient cardiac monitoring in daily life. The open-source code is available at https://***/CHENJIAR3/ECG_Denoising/tree/Linfei.

关键词： Electrocardiography Monitoring Noise Biomedical monitoring Electrodes Noise reduction Diffusion models Accuracy signal reconstruction signal processing algorithms Cardiac surveillance diffusion model signal conditioning signal reconstruction single-arm electrocardiogram (ECG)

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Learning-Based Tracking-Before-Detect for Unconstrained Indoor Human Tracking Using RF signal

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第6期24卷 4777-4793页

作者： Wu, Zhi Zhang, Dongheng Shang, Zixin Yuan, Yuqin Gong, Hanqin Wang, Binquan Lu, Zhi Li, Yadong Hu, Yang Sun, Qibin Chen, Yan Univ Sci & Technol China Sch Cyber Sci & Technol Hefei 230026 Peoples R China Univ Washington Dept Elect & Comp Engn Seattle WA 98195 USA

Human tracking plays a crucial role in various wireless sensing applications. However, recent advancements have primarily focused on constrained experimental scenarios with less interference, often involving a few individuals performing actions in an empty space without obstacles. In empirical unconstrained scenarios, such as daily office scenes, severe interference and attenuation caused by chaotic environments is inevitable which results in dramatic performance degradation. In this paper, we introduce TBDNet, which incorporates tracking-before-detect (TBD) from conventional signal processing into learning-based models, achieving impressive tracking performance in unconstrained scenarios. TBDNet follows first-track-then-detect pipeline. It maps input heatmap sequence into high-level frame-wise features to adapt the time-varying intensity distribution and motion pattern of targets. After that, the temporal information is accumulated in feature space to obtain trace proposals. We then predict the accurate positions and probability of traces at each timestamp. To assess the efficiency of TBDNet, we collect and release the first RF-UNIT (RF-based Unconstrained Indoor Tracking) dataset, which comprises 4,030,880 radar heatmaps and the corresponding tracking annotations under 6 different scenarios. To our knowledge, RF-UNIT is the first dataset for RF-based human tracking in unconstrained scenes. We anticipate that TBDNet and the RF-UNIT dataset will significantly contribute to the advancement of RF-based sensing technologies.

关键词： Radar tracking Target tracking Tracking Sensors Radar Interference Radar applications signal processing algorithms Heating systems signal processing Wireless sensing RF-based human tracking tracking-before-detect unconstrained scenarios

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Moving Target Detection With SNR Diversity for Distributed Coherent Aperture Radar on Moving Platforms

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第4期74卷 6346-6359页

作者： Zhou, Dingsen Yang, Minglei Lian, Hao Ma, Teng Greco, Maria Sabrina Gini, Fulvio Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Univ Pisa Dept Informat Engn I-56122 Pisa Italy

Distributed coherent aperture radar (DCAR) on moving platforms offers significant advantages of high mobility, long-range and high-power detection through coherent synthetic processing. However, radars may encounter signal-to-noise ratio (SNR) diversity issues in practice due to the variations in their locations, transmit powers, and time durations. Consequently, the detection performance of the DCAR can be severely degraded when the SNRs in each channel are different. Besides, the dynamic nature of platform motion poses challenges to coherent-on-receive detection due to the inconsistent Doppler frequencies across channels. In this paper, we consider the signal model incorporating the SNR diversity for DCAR on moving platforms. Subsequently, we propose two optimal channel weighting detection schemes for the moving target, including the weighted coherent-on-receive synthesis (W-CoRS) scheme and the weighted coherent-on-transmit/receive synthesis (W-CoT/RS) scheme, with both schemes aiming to maximize the output SNR. Notably, the two-stage W-CoRS scheme reduces communication costs by transmitting only weighted signals from each radar unit to the fusion center. Further, to optimize the detection performance, a high-precision coherent parameters (CPs) estimation algorithm based on the iterative Chirp-Z transform (ICZT) is proposed to address the off-grid issue of the Doppler frequency. At the same time, the proposed algorithm possesses the ability to accurately estimate the Doppler frequency offsets for each channel. Simulations demonstrate that the proposed schemes outperform the conventional DCAR (C-DCAR) in scenarios involving SNR diversity.

关键词： Radar signal to noise ratio Doppler effect Radar detection Estimation Doppler radar signal processing algorithms Frequency estimation Radar cross-sections Channel estimation Channel weighting detection CPs estimation DCAR moving platforms moving target SNR diversity

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Windowed Double-FFT Algorithm for SpO2 Measurement in Areas of Low Vascular Density

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IEEE SENSORS JOURNAL 2025年第1期25卷 904-918页

作者： Jin, Rencheng Hu, Peihao Huang, Qipeng Zhou, Zijian Zheng, Yinghua Dalian Univ Technol Ctr Micro & Nanosyst Sch Mech Engn Dalian 116024 Liaoning Peoples R China Dalian Univ Technol Cent Hosp Nursing Dept Dalian 116024 Liaoning Peoples R China

To effectively monitor the status of critically ill patients, particularly in clinical scenarios such as the prevention of pressure injury (PI), it is crucial to accurately measure blood oxygen saturation (SpO2) at sites with low vascular density. Traditional reflective SpO2 sensor algorithms often yield invalid measurements and poor accuracy in these areas. To address these challenges, this study proposes a novel photoplethysmography (PPG) signal processing algorithm that combines windowing techniques with a double-fast Fourier transform (FFT). The algorithm significantly reduces spectral leakage impacts on the amplitude of both alternating current (ac) and direct current (dc) components, thereby greatly enhancing the precision of SpO2 measurements. In addition, it innovatively introduces harmonic-based feature extraction technology, which more effectively and accurately extracts ac and dc components, enabling precise SpO2 measurements. The experimental results demonstrate that, compared to existing methods, the proposed algorithm improves SpO2 measurement effectiveness to over 95%, enhances the R-value stability by 50%, and reduces the mean absolute error (MAE) by 65% compared to standard fingertip values. These results clearly confirm the substantial benefits of the algorithm in enhancing the effectiveness, stability, and accuracy of SpO2 measurement in areas of low vascular density.

关键词： Sensors Blood Density measurement Accuracy signal processing algorithms Feature extraction Area measurement Monitoring Fast Fourier transforms Current measurement Fast Fourier transform (FFT) harmonic low vascular density sites photoplethysmography (PPG) spectral leakage windowing technique

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DL-Based ISAC via Tensor Analysis in Massive MIMO-OFDM Systems With Spatial-Frequency Wideband Effects

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IEEE INTERNET OF THINGS JOURNAL 2025年第5期12卷 5093-5108页

作者： Du, Jianhe Ma, Hang Yu, Zhiqiang Li, Xingwang Mumtaz, Shahid Yuen, Chau Commun Univ China State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China Hongyun Honghe Tobacco Grp Co Ltd Kunming Cigarette Factory Kunming 650231 Yunnan Peoples R China Yunnan Minzu Univ Sch Math & Comp Sci Kunming 650500 Peoples R China Henan Polytech Univ Sch Phys & Elect Informat Engn Jiaozuo Chin Myanmar Nottingham Trent Univ Dept Comp Sci Nottingham NG1 4FQ England Data Machine Elite Nottingham NG1 4FQ England Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

In this article, we propose a novel integrated sensing and communication (ISAC) algorithm for massive multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with spatial-frequency wideband (SFW) effects. To obtain high accuracy of channel state information (CSI), the proposed algorithm initially utilizes a deep neural network (DNN) for channel estimation. Then, the estimated channel is expressed as a third-order low-rank tensor model, on which the canonical polyadic (CP) decomposition is performed to obtain three factor matrices. These factor matrices hold the information pertaining to channel parameters. By fitting the constructed tensor model, channel parameters, such as Angles of Departure (AoDs), Angles of Arrival (AoAs), time delay, and complex gains, can be extracted. Ultimately, the positions of mobile station (MS) and scattering points are determined by utilizing the mapping relationship between the channel parameters and position coordinates. In contrast to existing algorithms, the proposed algorithm delivers greater precision in both channel estimation and positioning. The simulation results demonstrate that the proposed algorithm maintains outstanding ISAC performance, persisting even with diminished compression rate. Furthermore, the proposed algorithm proves effective in more complex scenarios lacking a line-of-sight (LOS) path.

关键词： Channel estimation Terahertz communications Tensors Massive MIMO signal processing algorithms Estimation Millimeter wave communication Training Integrated sensing and communication Accuracy Deep neural network (DNN) integrated sensing and communication (ISAC) spatial-frequency wideband (SFW) tensor

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Joint Node Selection and Resource Allocation Optimization for Cooperative Sensing With a Shared Wireless Backhaul

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 67-82页

作者： Chen, Mingxin Zhao, Ming-Min Liu, An Li, Min Shi, Qingjiang Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Prov Key Lab Multimodal Commun Networks & Hangzhou 310027 Peoples R China Tongji Univ Sch Software Engn Shanghai 201804 Peoples R China Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China

In this paper, we consider a cooperative sensing framework in the context of future multi-functional network with both communication and sensing ability, where one base station (BS) serves as a sensing transmitter and several nearby BSs serve as sensing receivers. Each receiver receives the sensing signal reflected by the target and communicates with the fusion center (FC) through a wireless multiple access channel (MAC) for cooperative target localization. To improve the localization performance, we present a hybrid information-signal domain cooperative sensing (HISDCS) design, where each sensing receiver transmits both the estimated time delay/effective reflecting coefficient and the received sensing signal sampled around the estimated time delay to the FC. Then, we propose to minimize the number of channel uses by utilizing an efficient Karhunen-Lo & eacute;ve transformation (KLT) encoding scheme for signal quantization and proper node selection, under the Cram & eacute;r-Rao lower bound (CRLB) constraint and the capacity limits of MAC. A novel matrix-inequality constrained successive convex approximation (MCSCA) algorithm is proposed to optimize the wireless backhaul resource allocation, together with a greedy strategy for node selection. Despite the high non-convexness of the considered problem, we prove that the proposed MCSCA algorithm is able to converge to the set of Karush-Kuhn-Tucker (KKT) solutions of a relaxed problem obtained by relaxing the discrete variables. Besides, a low-complexity quantization bit reallocation algorithm is designed, which does not perform explicit node selection, and is able to harvest most of the performance gain brought by HISDCS. Finally, numerical simulations are presented to show that the proposed HISDCS design is able to significantly outperform the baseline schemes.

关键词： Sensors Receivers Location awareness Quantization (signal) Transmitters signal processing algorithms Backhaul networks Accuracy Delay effects Wireless sensor networks Cooperative sensing limited backhaul multiple access channel node selection

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Near-Optimal MIMO Detection Using Gradient-Based MCMC inDiscreteSpaces

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 584-600页

作者： Zhou, Xingyu Liang, Le Zhang, Jing Wen, Chao-Kai Jin, Shi Southeast Univ Natl Mo bile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Natl Sun Yat Sen Univ Inst Commun Engn Kaohsiung 80424 Taiwan

The discrete nature of transmitted symbols poses challenges for achieving optimal detection in multiple-input multiple-output (MIMO) systems associated with a large number of antennas. Recently, the combination of two powerful machine learning methods, Markov chain Monte Carlo (MCMC) sampling and gradient descent, has emerged as a highly efficient solution to address this issue. However, existing gradient-based MCMC detectors are heuristically designed and thus are theoretically untenable. To bridge this gap, we introduce a novel sampling algorithm tailored for discrete spaces. This algorithm leverages gradients from the underlying continuous spaces for acceleration while maintaining the validity of probabilistic sampling. We prove the convergence of this method and also analyze its convergence rate using both MCMC theory and empirical diagnostics. On this basis, we develop a MIMO detector that precisely samples from the target discrete distribution and generates posterior Bayesian estimates using these samples, whose performance is thereby theoretically guaranteed. Furthermore, our proposed detector is highly parallelizable and scalable to large MIMO dimensions, positioning it as a compelling candidate for next-generation wireless networks. Simulation results show that our detector achieves near-optimal performance, significantly outperforms state-of-the-art baselines, and showcases resilience to various system setups.

关键词： Detectors Monte Carlo methods Symbols Convergence Complexity theory Vectors Transmitting antennas signal processing algorithms Wireless networks Modulation MIMO detection Markov chain Monte Carlo gradient descent Langevin algorithms discrete sample space

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