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Graph-Based Maximum Connected-Component Learning Algorithm for Small Target Detection in Maritime Radars

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第1期61卷 250-265页

作者： Bai, Xiaohui Xu, Shuwen Guo, Zixun Shui, Penglang Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China

Anomaly detection needs to learn one-class classifiers from normal instances in observation or feature spaces. In the Neyman-Pearson criterion, the design of one-class classifiers boils down to finding the minimal-volume decision region subject to the error probability of normal instances no larger than a desired false alarm rate. The theoretical solution to this design problem is the probability density function (pdf) level set of normal instances. In low-dimensional feature spaces, by combining training samples with the convexity regularity on decision regions, the convexhull learning algorithm is a technique for designing one-class classifiers. In order to overcome its dimension limitation and the mismatch of convexity to the level sets of a multimodal pdf, this article considers the approach to replace the convexity by the connectivity to regularize decision regions. A fast graph-based maximum connected-component learning algorithm is proposed to design one-class classifiers in high-dimensional feature spaces, which exploits the fast maximum connected-component search algorithm in a large-scale undirected graph. Moreover, for the application of sea-surface small target detection, the proposed algorithm combines ten-dimensional features to design feature-based detectors. Experimental results on the recognized radar database indicate the effectiveness of the proposed algorithm.

关键词： Training Detectors Level set Feature extraction Classification algorithms signal processing algorithms Radar Anomaly detection convexhull learning algorithm feature-based detection graph-based maximum connected-component (GMCC) learning algorithm sea-surface small target detection

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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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Mosaic Tracking: Lightweight Batch Video Frame Awareness Multitarget Encryption Scheme Based on a Novel Discrete Tabu Learning Neuron and YoloV5

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IEEE INTERNET OF THINGS JOURNAL 2025年第4期12卷 4038-4049页

作者： Mou, Jun Zhang, Zheyi Zhou, Nanrun Zhang, Yushu Cao, Yinghong Dalian Polytech Univ Sch Informat Sci & Engn Dalian 116034 Peoples R China Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China

With the popularity of surveillance devices, the security of surveillance video has attracted much attention, and three key issues need to be solved. The videos cannot be synchronized with their encryption effects, while full encryption does not meet the current trend of lightweight algorithms, and customized encryption for multiple specific targets is rarely seen. Inspired by this, a lightweight batch video frame awareness multitarget encryption scheme based on a novel discrete Tabu learning neuron (DTLN) and YoloV5 is designed in this article, the DTLN is in hyperchaotic state within a great range of parameters, which ensures the diversity of key selection and security. At the same time, the coexistence of homogeneous attractors is found, and such attractors are difficult to be successfully recognized by the parameter recognition algorithm, which increases the difficulty for the attacker to obtain the key. The mosaic tracking scheme designed by YoloV5 network can lightweightly encrypt batch frames of multiple types and targets, and users can also customize the encryption targets according to their needs. The simulation results show that the encryption scheme can realize lightweight encryption of multitype and multitarget, and performs well in all the security performance indexes, and has certain advantages compared with other video encryption schemes in terms of performance and functionality.

关键词： Encryption YOLO Surveillance Neurons Internet of Things Target recognition signal processing algorithms Complexity theory Training Target tracking Batch encryption lightweight encryption mosaic tracking Tabu learning neuron (TLN) target awareness YoloV5

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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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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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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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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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Accelerated Distributed Stochastic Nonconvex Optimization Over Time-Varying Directed Networks

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2196-2211页

作者： Chen, Yiyue Hashemi, Abolfazl Vikalo, Haris Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

Distributed stochastic nonconvex optimization problems have recently received attention due to the growing interest of signal processing, computer vision, and natural language processing communities in applications deployed over distributed learning systems (e.g., federated learning). We study the setting where the data is distributed across the nodes of a time-varying directed network, a topology suitable for modeling dynamic networks experiencing communication delays and straggler effects. The network nodes, which can access only their local objectives and query a stochastic first-order oracle to obtain gradient estimates, collaborate to minimize a global objective function by exchanging messages with their neighbors. We propose an algorithm, novel to this setting, that leverages stochastic gradient descent with momentum and gradient tracking to solve distributed nonconvex optimization problems over time-varying networks. To analyze the algorithm, we tackle the challenges that arise when analyzing dynamic network systems that communicate gradient acceleration components. We prove that the algorithm's oracle complexity is O(1/epsilon(1.5)), and that under Polyak-Lojasiewicz condition the algorithm converges linearly to a steady error state. The proposed scheme is tested on several learning tasks: a nonconvex logistic regression experiment on the MNIST dataset, an image classification task on the CIFAR-10 dataset, and an NLP classification test on the IMDB dataset. We further present numerical simulations with an objective that satisfies the PL condition. The results demonstrate superior performance of the proposed framework compared to the existing related methods.

关键词： Stochastic processes Optimization signal processing algorithms Convergence Computational modeling Heuristic algorithms Complexity theory Linear programming Distance learning Directed graphs Decentralized nonconvex optimization stochastic nonconvex optimization time-varying directed network

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Optimizing Clustered Cell-Free Networking for Sum Ergodic Capacity Maximization With Joint processing Constraint

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第1期24卷 571-584页

作者： Xia, Funing Wang, Junyuan Dai, Lin Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ Inst Adv Study Coll Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 201804 Peoples R China City Univ Hong Kong Dept Elect Engn SAR Hong Kong Peoples R China

Clustered cell-free networking has been considered as an effective scheme to trade off between the low complexity of current cellular networks and the superior performance of fully cooperative networks. With clustered cell-free networking, the wireless network is decomposed into a number of disjoint parallel operating subnetworks with joint processing adopted inside each subnetwork independently for intra-subnetwork interference mitigation. Different from the existing works that aim to maximize the number of subnetworks without considering the limited processing capability of base-stations (BSs), this paper investigates the clustered cell-free networking problem with the objective of maximizing the sum ergodic capacity while imposing a limit on the number of user equipments (UEs) in each subnetwork to constrain the joint processing complexity. By successfully transforming the combinatorial NP-hard clustered cell-free networking problem into an integer convex programming problem, the problem is solved by the branch-and-bound method. To further reduce the computational complexity, a bisection clustered cell-free networking ((BCF)-F-2-Net) algorithm is proposed to decompose the network hierarchically. Simulation results show that compared to the branch-and-bound based scheme, the proposed (BCF)-F-2-Net algorithm significantly reduces the computational complexity yet achieves nearly the same network decomposition result. Moreover, our (BCF)-F-2-Net algorithm achieves near-optimal performance and outperforms the state-of-the-art benchmarks with up to 25% capacity gain.

关键词： Clustering algorithms Wireless networks Partitioning algorithms Interference Cellular networks Simulation signal processing algorithms Uplink Optimization Computer architecture Clustered cell-free networking sum ergodic capacity maximization joint processing constraint bisection algorithm

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Resource Allocation for User-Centric Cell-Free Massive MIMO Downlink

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IEEE SENSORS JOURNAL 2025年第1期25卷 1682-1693页

作者： Wang, Xuan Liu, Yi Li, Cong Xidian Univ State Key Lab Integrated Serv Network Xian 710071 Peoples R China Acad Space Elect Informat Technol Xian 710100 Peoples R China

Given the strong macro-diversity gain, cell-free massive multiple-input-multiple-output (MIMO) with many access points (APs) can provide a more uniform service level for randomly distributed users or user sensors (USs). There has, however, been little implemented in practical applications, due to the challenges of substantial backhaul overhead and high signal processing demands in cell-free massive MIMO systems. In this article, we propose a resource allocation scheme that incorporates the effective algorithms for AP selection, pilot assignment, and power allocation, to improve the performance of user-centric cell-free massive MIMO systems. In particular, a grouping-based AP selection algorithm is investigated to select APs with significant gain contributions to USs. Based on this, we propose an improved K-means clustering (I-KMC)-based pilot assignment algorithm to reduce pilot contamination caused by pilot reuse. To improve the reliability of data transmission, we present two power allocation algorithms to maximize the achievable sum-rate, namely sequential convex approximation in combination with geometric programming (SCA-GP) approach, and nonmonotone accelerated proximal gradient (NAPG) approach with line search. Results demonstrate that the proposed scheme achieves similar sum-rate performance to the scenario where all USs are served by all APs, but with significantly lower power consumption.

关键词： Massive MIMO Resource management Downlink Backhaul networks signal processing algorithms Sensors Clustering algorithms Uplink Fading channels Array signal processing Cell-free massive multiple-input-multiple-output (MIMO) grouping-based access point (AP) selection improved K-means clustering (I-KMC) nonmonotone accelerated proximal gradient (NAPG) sequential convex approximation in combination with geometric programming (SCA-GP)

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