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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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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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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 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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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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A Data-Driven Control Algorithm With Time Delay Compensation Based on Optimized Recursive Adaptive Identification for Magnetic Levitation Systems

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 12922-12936页

作者： Li, Zhen Jin, Shangtai Wei, Yuzhou Ji, Honghai Hou, Zhongsheng Beijing Jiaotong Univ Inst Adv Control Syst Beijing 100044 Peoples R China Beijing Inst Technol Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401120 Peoples R China North China Univ Technol Sch Elect & Control Engn Beijing 100093 Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China

This study addresses the challenge of precise control of the maglev system under the condition of rapid time-varying dynamics and large time delay. Maglev technology has been applied in many fields, such as maglev train and magnetic bearing, for its non-contact operation, low maintenance cost and high energy efficiency. However, the inherent nonlinear characteristics and delayed response characteristics of the magnetic levitation system make it very difficult to control. In practical scenarios, it is difficult to establish an accurate magnetic suspension system model, which limits the use of model-based control methods. PID control method is also difficult to guarantee the control performance when dealing with unmeasurable load disturbance and system delay scenarios. This study proposes a data-driven control solution, which integrates an optimized recursive identification algorithm and an adaptive delay compensation algorithm. Uniquely, the control solution achieves real-time iterative optimization with only a few input and output data, reducing the data storage resource requirements, avoiding offline training, and ensuring real-time performance and efficient resource utilization in practical applications. Practitioners can use this new method to enhance the performance and reliability of maglev applications, such as maglev trains, industrial processes and precision instruments, so as to achieve more robust and adaptive control in the face of rapid dynamic changes and large time delays.

关键词： Magnetic levitation Heuristic algorithms Delays Mathematical models Delay effects Data models Time-varying systems signal processing algorithms Magnetic levitation vehicles Stability analysis Magnetic levitation systems model-free adaptive control time delay compensation recursive identification dynamic linearization

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A Novel Clutter Suppression Algorithm for Airborne Radar via Subspace Projection

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IEEE SENSORS JOURNAL 2025年第1期25卷 962-970页

作者： Zhang, Xinying Wang, Tong Cui, Weichen Wang, Degen Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Beijing Aerosp Automatic Control Inst Natl Key Lab Sci & Technol Aerosp Intelligence Con Beijing 100854 Peoples R China Southwest China Res Inst Elect Equipment Chengdu 610036 Peoples R China

Strong ground clutter submerges the moving targets and hinders the radar image analysis. An effective technique called space-time adaptive processing (STAP) is used to suppress clutter in airborne radar systems but has the disadvantage of large training sample requirements. Sparse recovery STAP (SR-STAP) has the benefit of improving the algorithm performance when the training sample size is small. However, the computational burden and storage space restrict the practical application of SR-STAP. To address this issue, a fast subspace-based SR-STAP algorithm is proposed in this article. Using the Bayesian framework, the proposed algorithm selects the most matched atoms to span a clutter subspace by minimizing the likelihood function. In comparison with the existing SR-STAP algorithm, the proposed algorithm avoids the time-consuming sparse reconstruction process. Moreover, the estimated subspace can be applied to gain-phase error calibration. The proposed algorithm is experimentally demonstrated to have superior performances in terms of clutter suppression, target detection, and computational complexity.

关键词： Clutter Vectors Noise Training Sensors Airborne radar signal processing algorithms Object detection Bayes methods Atoms Clutter suppression gain-phase errors space-time adaptive processing (STAP) sparse recovery subspace estimated

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Tensor Completion Network for Visual Data

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 386-400页

作者： Wang, Xiang-Yu Li, Xiao-Peng Sidiropoulos, Nicholas D. So, Hing Cheung City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Shenzhen Univ State Key Lab Radio Frequency Heterogeneous Integr Shenzhen 518060 Peoples R China Univ Virginia Dept Elect & Comp Engn Charlottesville VA 22904 USA

Tensor completion aims at filling in the missing elements of an incomplete tensor based on its partial observations, which is a popular approach for image inpainting. Most existing methods for visual data recovery can be categorized into traditional optimization-based and neural network-based methods. The former usually adopt a low-rank assumption to handle this ill-posed problem, enjoying good interpretability and generalization. However, as visual data are only approximately low rank, handcrafted low-rank priors may not capture the complex details properly, limiting the recovery performance. For neural network-based methods, despite their impressive performance in image inpainting, sufficient training data are required for parameter learning, and their generalization ability on the unseen data is a concern. In this paper, combining the advantages of these two distinct approaches, we propose a tensor Completion neural Network (CNet) for visual data completion. The CNet is comprised of two parts, namely, the encoder and decoder. The encoder is designed by exploiting the CANDECOMP/PARAFAC decomposition to produce a low-rank embedding of the target tensor, whose mechanism is interpretable. To compensate the drawback of the low-rank constraint, a decoder consisting of several convolutional layers is introduced to refine the low-rank embedding. The CNet only uses the observations of the incomplete tensor to recover its missing entries and thus is free from large training datasets. Extensive experiments in inpainting color images, grayscale video sequences, hyperspectral images, color video sequences, and light field images are conducted to showcase the superiority of CNet over state-of-the-art methods in terms of restoration performance.

关键词： Tensors Visualization Neural networks Vectors Matrix decomposition Image restoration Decoding Videos signal processing algorithms Matrix converters Tensor completion CP decomposition unrolling encoder-decoder neural network image inpainting visual data recovery

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Joint Activity Detection and Channel Estimation in Mixed-Fronthaul Cell-Free Massive MIMO

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第3期74卷 4117-4131页

作者： Zhao, Tianyu Chen, Shuyi Chen, Hsiao-Hwa Guo, Qing Harbin Inst Technol Commun Res Ctr Harbin 150001 Peoples R China Natl Cheng Kung Univ Dept Engn Sci Tainan 70101 Taiwan

Cell-free massive multiple-input multiple-output (CFmMIMO) is one of the key enabling technologies for massive machine type communications (mMTC). While CFmMIMO can achieve a macro diversity gain in mMTC, its fronthaul link capacity severely limits system performance. To balance fronthaul overhead and detection/estimation performance, in this paper we investigate joint user activity detection and channel estimation (JADCE) and develop a framework for uplink grant-free CFmMIMO systems based on mixed-quantization fronthaul compression. First, we model the JADCE problem as a mixed-quantization compressing sensing problem, making use of sporadic transmission nature of devices in mMTC. A multiple measurement vector (MMV) generalized approximate message passing (GAMP) algorithm is proposed to improve the performance of activity detection and channel estimation. Unknown priori parameters of the GAMP algorithm can be estimated by an expectation-maximization algorithm. In addition, we design a threshold detection rule at central processing unit (CPU) side and derive a lower bound of active user detection performance in a centralized CFmMIMO system. Numerical results reveal that the proposed mixed-quantization scheme can strike a good balance between fronthaul overhead and detection/estimation performance, verifying that a CFmMIMO with mixed-quantization scheme can perform well in activity detection and channel estimation by selecting an appropriate number of quantization bits.

关键词： Channel estimation Quantization (signal) Uplink Estimation Approximation algorithms signal processing algorithms Performance evaluation Massive MIMO Vectors Noise Cell-free massive MIMO activity detection channel estimation massive machine-type communication grant-free random access

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Fast Subspace and DOA Estimation Method for the Case of High-Dimensional and Small Samples

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第3期74卷 3958-3975页

作者： Zhang, Xuejun Feng, Dazheng Zheng, Weixing Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Western Sydney Univ Sch Comp Data & Math Sci Sydney NSW 2751 Australia

It is well-known that classical direction of arrival (DOA) estimation methods work well in the case of large samples. However, these methods may be theoretically invalid in the case of small samples, which frequently occur in large array systems. Such a large array has two effects: i) The number of samples is relatively quite small, and ii) the dimension of samples is very large. To handle the above problems, a more appropriate method for solving DOA estimators in the case of high-dimensional and small samples is proposed in this paper. First, considering the special structure of received samples, an alternative well-estimated second-order statistic, known as the Gram matrix, is originally constructed to better utilize the spatial and statistical information of signals and noise contained by small samples. Second, two novel methods for estimating the number of targets are derived by combining the Gram matrix and information-theoretic criteria. Third, a novel object function and the corresponding algorithm based on the Gram matrix are designed to estimate the signal subspace more efficiently, and then the DOAs of targets are obtained by multiple signal classification methods. In particular, the theoretical analysis indicates that the improved signal subspace estimation algorithm only needs to decompose the low-dimensional Gram matrix instead of the high-dimensional sample covariance matrix. Finally, simulation results are provided to demonstrate the high accuracy and lower computational complexity of the proposed methods in the case of high-dimensional and small samples.

关键词： Direction-of-arrival estimation Estimation Prediction algorithms Noise Covariance matrices Classification algorithms signal processing algorithms Multiple signal classification Accuracy Vectors High-dimensional and small samples gram matrix direction of arrival number of targets estimation method subspace estimation algorithm

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