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Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Yonghao Zhou Zhengang Wan Jiangsu University of Science and Technology Zhenjiang China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

In this paper, an improved fractional order LMS filtering algorithm is proposed. By dynamically adjusting the step size and fractional order, the parameters are optimized in real time according to the errors, and the convergence speed and steady-state accuracy are improved. The forgetting factor is introduced to enhance the tracking ability of time-varying signal. The simulation results show that the improved algorithm performs well in the processing of complex Marine acoustic signals containing noise, and has a wide application prospect in the fields of Marine communication and detection.

关键词： Heuristic algorithms Simulation signal processing algorithms Filtering algorithms Acoustics Real-time systems Steady-state Smart grids Noise measurement Convergence

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Direction of Arrival Estimation Based on AR Modeling in the Presence of Colored Noise

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IEEE ACCESS 2025年 13卷 62427-62433页

作者： Bakhshandeh Babarsad, Sina Mahmoudi, Alimorad Namjoo, Ehsan Shahid Chamran Univ Ahvaz Fac Engn Dept Elect Engn Ahvaz *** Iran

This paper investigates the problem of estimating the direction of arrival considering colored noise at the sensors. To this aim, we model the recieved signal at each time instant with an AR system with rank equal to the number of sources and we simultaneously estimated signal and noise parameters by solving a quadratic eigenvalue problem. Simulation results demonstrate that utilizing root mean square error as the accuracy metric, the proposed method outperforms existing subspace-based approaches, particularly when the targets are close to eachother.

关键词： Noise Direction-of-arrival estimation Sensors Colored noise Maximum likelihood estimation Transforms signal processing algorithms Multiple signal classification Eigenvalues and eigenfunctions White noise AR modeling colored noise DOA estimation subspace-based methods

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Cramer-Rao Lower Bounds for Unconstrained and Constrained Quaternion Parameters

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 508-518页

作者： Sun, Shuning Xu, Dongpo Diao, Qiankun Mandic, Danilo P. Northeast Normal Univ Sch Math & Stat Key Lab Appl Stat MOE Changchun 130024 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

The Cramer-Rao lower bound (CRLB) is a fundamental result in statistical signal processing, however, the CRLB for quaternion parameters is not yet established. To this end, we develop the theory of quaternion Cramer-Rao lower bound (QCRLB), based on the generalized Hamilton-real (GHR) calculus. For generality, this is achieved in a way that conforms with the real and complex CRLB. We first provide the properties of the quaternion covariance matrix and the quaternion Fisher information matrix (FIM), paving the way for the derivation of the QCRLB. This serves as a basis for the formulation of the QCRLB without constraints and a criterion for determining whether the QCRLB is attained. We also establish the QCRLB for constrained quaternion parameters, including both nonsingular and singular cases of the quaternion FIM. These broaden the theoretical framework and enhance its applicability to diverse practical scenarios. The practical efficacy of the QCRLB is demonstrated through two illustrative examples. Numerical validations confirm that the maximum-likelihood estimator (MLE) attains the QCRLB for the linear model, and the quaternion gradient ascent (QGA) algorithm achieves the QCRLB at each iteration with theoretical guarantees. We also propose the quaternion constrained scoring (QCS) algorithm, which converges in one step in the linear constrained MLE case, for the linear model. These results significantly contribute to both the theory and practical application of quaternion signal processing, bringing valuable insights into the quaternion parameter estimation.

关键词： Quaternions Vectors signal processing Lower bound Calculus signal processing algorithms Covariance matrices Maximum likelihood estimation Three-dimensional displays Image color analysis Quaternion Cramer-Rao lower bound quaternion constrained Cramer-Rao lower bound quaternion Fisher information matrix quaternion parameter estimation GHR calculus

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Cognitive Radar Subpulses Waveform Design via Online Greedy Search

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 1122-1137页

作者： Wang, Yi Yu, Xianxiang Yang, Jing Cui, Guolong Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China

This paper proposes a cognitive radar subpulse waveform design (CRSWD) approach including a smart acquirement process of some a prior information and multi-sequences optimization algorithm against multiple mainlobe interrupted sampling repeater jamming (ISRJ). Specifically, the radar first interacts with the environment on a pulse-by-pulse basis to quest for radar survival window (RSW) dynamically. Hence, a novel RSW searching method incorporating the greedy strategy is devised, where a reasonable value function is defined for measuring the anti-jamming and detection capabilities. Based on the estimated RSW information, orthogonal shielding and probing subpulses are strategically positioned for confusing the jammer and then detecting targets, respectively. To this respect, a non-convex optimization problem based on the peak to-sidelobe level (PSL) criterion and peak-to-average ratio (PAR) constraints is formulated for designing orthogonal probing and shielding waveforms with optimized RSW knowledge. A fast iterative methodology based on block coordinate descent (BCD) and majorize-minimization (MM) framework is proposed with the convergence performance ensured. Numerical simulations demonstrate that the proposed framework can effectively acquire jamming-resistant RSW in the presence of multiple targets and ISRJ with different parameters and achieve reliable detection outperforming some counterparts. Experiments are conducted to further verify its engineering feasibility.

关键词： Jamming Radar Optimization Resource management Repeaters Cognitive radar Training Noise Vectors signal processing algorithms Cognitive radar (CR) interrupted sampling repeater jamming (ISRJ) radar survival window (RSW) greedy strategy majorize-minimization (MM) waveform design

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Primal-Dual Fixed Point algorithms Based on Adapted Metric for Distributed Optimization With Bounded Communication Delays

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2212-2227页

作者： Su, Yanxu Wang, Qingling Sun, Changyin Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China Minist Educ Engn Res Ctr Autonomous Unmanned Syst Technol Hefei 230601 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Peoples R China

This article concentrates on distributed optimization over networks with communication delays. Each subsystem in the network performs its local updates by using the information received from its neighbors, be it possibly outdated. The communication delays with respect to different neighbors are assumed to be arbitrary but bounded. The objective function consists of a twice differentiable coupling term and an aggregated private term. The private function of each subsystem is the sum of two possibly nonsmooth terms, one of which is composed of a linear mapping. We propose a primal-dual fixed point algorithm framework based on the adapted metric for two scenarios where the coupling among subsystems is only enacted by the global objective function and enforced both by the global objective function and the linear mapping. The adapted metric method utilizes an adequate quadratic approximation of the global objective function as the updating step-size to exploit the second-order information. Under some mild assumptions, the convergence of the proposed algorithms is rigorously analyzed based on the quasi-Fej & eacute;r monotonicity. The numerical simulation verifies the correctness and effectiveness of the proposed algorithms.

关键词： Delays Optimization Couplings Measurement Convergence Linear programming Vectors Synchronization Symmetric matrices signal processing algorithms Adapted metric methods asynchronous algorithms distributed optimization primal-dual fixed point (PDFP) algorithms

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High-Precision UWB TDOA Localization Algorithm Based on UKF-FNN-CHAN-RIC

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Wang, Ershen Wang, Yongkang Zhang, Shuning Xu, Song Chen, Yunhao Wang, Yanwen Yu, Tengli Lei, Hong Shenyang Aerosp Univ Coll Elect Informat Engn Shenyang 110136 Peoples R China Southern Univ Sci & Technol Sch Automat & Intelligent Mfg Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Guangdong Prov Key Lab Fully Actuated Syst Control Shenzhen 518055 Peoples R China Yunnan Key Lab Unmanned Autonomous Syst Kunming 650500 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China Shenyang Aerosp Univ Coll Aeronaut & Astronaut Shenyang 110136 Peoples R China Shenyang Aircraft Design & Res Inst Shenyang 110035 Peoples R China

Ultrawideband (UWB) technology has garnered significant attention due to its extensive applications in indoor positioning. However, its performance is susceptible to interference from environmental factors. In response to the positioning accuracy issues in time difference of arrival (TDOA) scenarios, this article proposes an integrated TDOA-UKF-FNN-Chan-RIC algorithm that incorporates the unscented Kalman filter (UKF), feedforward neural networks (FNNs), Chan's algorithm, and redundant information correction (RIC) to effectively enhance the positioning accuracy and robustness of UWB systems. Initially, by investigating the redundant information within the UWB solution model in TDOA scenarios, the inter-relationship between redundant information and measurement vectors is revealed. Subsequently, a gradient descent error correction algorithm for redundant information suitable for TDOA scenarios is proposed, and UKF and FNN transformations are combined to ensure that the data meet the preconditions for algorithmic application. To address the stability issues of positioning algorithms based on implicit function solutions in TDOA scenarios, Chan's algorithm is employed to convert range difference measurements into range measurements, thereby enhancing computational robustness. Ultimately, the TDOA-UKF-FNN-Chan-RIC algorithm is proposed, and its effectiveness is validated through simulation and practical experiments. The experimental results demonstrate that this algorithm significantly improves positioning accuracy and robustness, achieving millimeter-level fixed-point accuracy and centimeter-level track accuracy in practical experiments.

关键词： Accuracy Ultra wideband technology Robustness Position measurement Distance measurement signal processing algorithms Kalman filters Three-dimensional displays Optimization Filtering Chan feedforward neural network (FNN) redundant information correction (RIC) time difference of arrival (TDOA) unscented Kalman filter (UKF) ultrawideband (UWB) positioning

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IoT-Based System Using IMU Sensor Fusion for Knee Telerehabilitation Monitoring

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IEEE SENSORS JOURNAL 2025年第7期25卷 11906-11914页

作者： El Fezazi, Mohamed Achmamad, Abdelouahad Jbari, Atman Jilbab, Abdelilah Mohammed V Univ Rabat Natl Grad Sch Arts & Crafts ENSAM Elect Syst Sensors & Nanobiotechnol Rabat 10100 Morocco

High costs and clinical limitations restrict access to rehabilitation services, especially in low- and middle-income countries. There is a growing need for affordable, home-based solutions that enable continuous remote monitoring of patient rehabilitation progress. This work proposes an Internet of Thing (IoT)-based system for knee movement monitoring during telerehabilitation. The system comprises wearable inertial measurement units (IMUs) integrated into an IoT architecture. This architecture leverages edge and cloud computing to facilitate remote monitoring and real-time feedback. A sensor fusion algorithm was implemented on the edge to estimate knee joint angle, and a cloud-based application was developed to extract kinematic parameters and assess rehabilitation outcomes. The system was implemented using system-on-chip (SoC) technology, allowing embedded signal processing and wireless communication in a compact and low-power design. Three experimental validation tests were conducted: One hardware test evaluating the performance of the proposed sensor fusion algorithm;goniometer-based static test assessing the impact of environmental interference on system accuracy;dynamic test involving rehabilitation exercises to validate system performance against a gold-standard video-based system in the home context. The results demonstrated that the proposed algorithm achieved an optimal trade-off between accuracy, computational efficiency, and resilience to magnetic distortions. The system showed acceptable accuracy, with an average root mean square error (RMSE) ranging from 3.08 degrees to 6.43 degrees across all exercises. These results are consistent with the current state of the art, highlighting the system's potential for objective and remote monitoring of knee movement in home-based rehabilitation.

关键词： Sensors Knee Logic gates Accuracy Sensor fusion Cloud computing signal processing algorithms Biomedical monitoring Wireless communication Sensor systems Embedded edge-processing inertial sensor fusion Internet of Things (IoT) motion capture (MoCap) system wearable wireless sensor

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Radar-PIM: Developing IoT Processors Utilizing processing-in-Memory Architecture for Ultrawideband-Radar-Based Respiration Detection

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IEEE INTERNET OF THINGS JOURNAL 2025年第1期12卷 515-530页

作者： Lee, Kyeongwon Jeon, Sangmin Lee, Kangju Lee, Woojoo Pedram, Massoud Chung Ang Univ Dept Intelligent Semicond Engn Seoul 06974 South Korea Univ Southern Calif Dept Elect & Comp Engn Los Angeles CA 90007 USA

The adoption of ultrawideband (UWB) radar technology in IoT and healthcare applications for respiration detection is rapidly expanding, opening up a wide array of potential use cases. Despite its burgeoning utility, the integration of UWB radar-based respiration detection in IoT end node devices faces significant challenges due to the memory-intensive nature of these tasks, which strain the capabilities of IoT processors. This article introduces a streamlined UWB radar-based respiration detection application designed for operation on IoT processors, emphasizing that when executed on conventional IoT processors, the limited processing power still results in significant data loss, underscoring the need for enhanced processing solutions. To address these challenges, we propose the adoption of processing-in-memory (PIM) technology and unveil the novel Radar-PIM architecture. This architecture is meticulously engineered to boost the efficiency of respiration detection while ensuring seamless integration with existing embedded processor frameworks. This article extensively describes the Radar-PIM architecture and its operational mechanisms. We further demonstrate its superior performance by implementing and empirically testing a Radar-PIM processor prototype. Next, we present an optimization strategy tailored for designing energy-efficient Radar-PIM processors, specifically adapted for diverse UWB radar-based respiration detection applications. For instance, a Radar-PIM processor prototype, optimized for a particular application, achieved approximately 42% energy savings compared to its unoptimized counterpart and delivered performance nearly three times greater than that of a multicore processor with equivalent power consumption. This demonstrates the transformative potential of our proposed solution in enhancing the capabilities of radar-based respiration detection systems for IoT end nodes.

关键词： Program processors Internet of Things Ultra wideband radar Computer architecture signal processing algorithms Random access memory Prototypes IoT processor impulse radio (IR)-ultrawideband (UWB) radar low-power designs respiration detection RISC-V

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Distributed Downlink Precoding for Cell-Free Massive MIMO: A Quasi-Neural Network Approach

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IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第2期73卷 1157-1168页

作者： Dai, Weijie Wang, Rui Liu, Junkai Jiang, Yi Fudan Univ Sch Informat Sci & Technol Dept Commun Sci & Engn Key Lab Informat Sci Electromagnet Waves MoE Shanghai 200438 Peoples R China

This paper proposes a novel downlink precoding method for a cell-free massive multiple-input multiple-output (CF-mMIMO) network, requiring no channel state information sharing between the access points via fronthaul links. By drawing analogies between a CF-mMIMO network and an artificial neural network, the proposed algorithm borrows the idea of backpropagation to train the precoders and the combiners through over-the-air ping-pong signaling between the access points and user equipments. It utilizes manifolds optimization to meet the per-AP power constraint and is named as distributed quasi-neural network precoding on manifold (DQNPM). The DQNPM algorithm can accommodate a large category of objective functions for fully distributed implementation. Numerical simulations show that our method outperforms the state-of-the-art approaches, and is robust against pilot contamination.

关键词： Precoding Downlink Manifolds Artificial neural networks Transmitting antennas signal to noise ratio signal processing algorithms Cell-free massive MIMO distributed precoding quasi-neural network Riemannian manifold

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Low Complexity and Mobility-Aware Robust Radio, Storage, Computing, and Cost Management for Cellular Vehicular Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第2期74卷 3327-3344页

作者： Abedi, Mohammad Reza Mokari, Nader Javan, Mohammad Reza Saeedi, Hamid Jorswieck, Eduard A. Zorba, Nizar Tarbiat Modares Univ ECE Dept Tehran 14115111 Iran Shahrood Univ Technol Fac Elect Engn Shahrood *** Iran Univ Doha Sci & Technol Coll Engn & Technol Doha 24449 Qatar TU Braunschweig Inst Commun Technol D-38106 Braunschweig Germany Qatar Univ Coll Engn Doha 2713 Qatar

In this paper, a novel two-time scale task offloading/delivery framework is presented which dynamically manages radio, storage, computing, and cost aspects through multiple Mobile Edge Computing (MEC) servers distributed across Road Side Units (RSUs), Base Stations (BSs), vehicles, and pedestrians. To enhance flexibility, we introduce a smart dynamic task partitioning and cooperative offloading/delivery mechanism, enabling users to offload task partitions to various MEC servers. Uncertainties stemming from users' high mobility and traffic variations may lead to unpredictable task offloading demands and Imperfect Channel State Information (I-CSI), potentially causing unsuccessful task offloading/delivery. In response, we propose a robust and cooperative two-time-scale task offloading/delivery approach, adept at handling demand uncertainty and I-CSI. The problem formulation involves a two-time scale Markov decision process, aimed at minimizing task offloading/delivery costs while maximizing task completion rates. Large time-scale slots incorporate intelligent RSU activation/deactivation, while short time-scale slots focus on task offloading/delivery. To efficiently implement this framework, we propose Federated Reinforcement Learning (FRL) algorithm based on Deep Deterministic Policy Gradient (DDPG);namely Low overhead Multi-Agent DDPG (LoMADDPG) where at each slot, all agents tune their Actor and Critic neural network weights with the help of Global Software-defined Networking (SDN) Controller (GSC) which acts as a global weight tuner. Simulation results illustrate that our proposed task offloading/delivery scheme can result in significant improvement in task completion rate and cost reduction.

关键词： Uncertainty Servers Resource management Optimization Costs Delays signal processing algorithms Machine learning algorithms Vehicle-to-everything Vehicle dynamics Mobile edge computing two-time scale task offloading/delivery RSU activation/deactivation demand and channel uncertainties task partitioning task migration

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