检索结果-内蒙古大学图书馆

A Simple and Efficient Method for Hybrid AOA and DTD Localization With Unknown Transmitter Location

IEEE signal processing LETTERS 2025年 32卷 401-405页

作者： Zou, Yanbin Xiao, Yangpeng Zhang, Weien Shantou Univ Dept Elect & Informat Engn Shantou 515063 Peoples R China

Recently, joint target and transmitter localization using differential time-delay (DTD) and angle-of-arrival (AOA) measurements has attracted researchers' interest. Due to the fact that three Euclidean norms exist in the DTD equation, the DTD equation is difficult to tackle directly. In this paper, we divide the joint localization problem into three subproblems, respectively, the AOA-only localization problem, the hybrid AOA and time-difference-of-arrival (TDOA) localization problem, and the hybrid AOA and time-delay (TD) localization problem with known transmitter location. Then, a two-stage algorithm is developed. In the first stage, solving the AOA-only localization problem provides initial estimates. In the second stage, alternatively and iteratively solving the problem of hybrid AOA and TDOA localization and the problem of hybrid AOA and TD localization provide the improved solutions. Simulation results validate that the proposed algorithm is superior to the existing constrained weighted least-squares (CWLS) algorithm when AOA noise variance is not sufficiently small. Index Term-Angle-of-arrival (AOA), differential time-delay (DTD), time-delay (TD), time-difference-of-arrival (TDOA), elliptic localization.

关键词： Location awareness Transmitters Mathematical models Noise Estimation signal processing algorithms Sensors Covariance matrices Three-dimensional displays Position measurement Angle-of-arrival (AOA) differential time-delay (DTD) time-delay (TD) time-difference-of-arrival (TDOA) elliptic localization

来源：评论

学校读者我要写书评

暂无评论

Improved Least Lncosh Based Fetal Electrocardiography Extraction in Alpha-Stable Noise

引用

IEEE signal processing LETTERS 2025年 32卷 676-680页

作者： Wang, Mengjia Ni, Bo Zhang, Jiacheng Luan, Shengyang Liu, Tao Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221116 Peoples R China Chengdu Univ Inst Higher Educ Sichuan Prov Key Lab Pattern Recognit & Intelligent Informat Pr Chengdu 610106 Peoples R China

Fetal electrocardiography (FECG) presents an important avenue for continuous fetal monitoring. Nonetheless, effectively extracting FECG from maternal electrocardiogram (MECG) is one considerable challenge due to its weaker amplitude compared to MECG and the non-Gaussian nature of background noise. In this letter, we introduce alpha-stable noise to model the realistic interference due to its high scalability. To improve the accuracy of FECG extraction under impulsive noise (alpha-stable noise with strong impulses), we introduce the least Lncosh algorithm (Llncosh) and the improved Llncosh algorithm (ILL) is proposed based on the Amplitude Hyperbolic Tangent Transformation (AHTT) to optimize the preset parameter. Moreover, Monte Carlo experiments are carried out to investigate the capabilities of LMS-like algorithms and the ILL algorithm for FECG extraction. The results demonstrate that the ILL algorithm outperforms the LMS-like ones with carefully selected parameters, particularly showcasing superior robustness against impulsive noise. This work holds significance both in the theoretical research of adaptive filtering and in its practical application for FECG extraction.

关键词： Noise signal processing algorithms Electrocardiography Cost function Adaptive filters Robustness Accuracy Vectors Nickel Monte Carlo methods LMS algorithm Llncosh algorithm fetal electrocardiogram alpha-stable noise impulsive noise

来源：评论

学校读者我要写书评

暂无评论

Two-Dimensional Successive Variational Mode Decomposition

引用

IEEE signal processing LETTERS 2025年 32卷 1865-1869页

作者： Faisal, Kazi Newaj Chandela, Ujjawal Sharma, Rishi Raj Mil Inst Sci & Technol Dept EECE Dhaka 1216 Bangladesh Def Inst Adv Technol Dept Elect Engn Pune 411025 India

This letter presents a 2D extension of the successive variational mode decomposition (2D-SVMD) method, which decomposes 2D signals into band-limited intrinsic mode functions without requiring prior knowledge of the number of modes. The proposed 2D-SVMD addresses the limitations of existing 2D variational mode decomposition (2D-VMD) and 2D variational mode decomposition with total variation (2D-TV-VMD) methods, which need initialization of the number of modes and can produce redundant or mixed modes if the number is not accurately specified. 2D-SVMD decomposes in a sequential manner which enhances the convergence rate of the decomposition process and also mitigates the extraction of extraneous modes, collectively resulting in reduced computational overhead. The proposed 2D-SVMD is also evaluated on synthetic and real-world images and compared to 2D-VMD and 2D-TV-VMD, demonstrating its superior performance in achieving natural decomposition of 2D signals.

关键词： signal processing algorithms Convergence Data mining Optimization Transforms Training Peptides Image reconstruction Artificial intelligence Two-dimensional displays 2D signal signal decomposition image decomposition 2D variational mode decomposition and successive variational mode decomposition

来源：评论

学校读者我要写书评

暂无评论

Computer Vision Enabled Calibration of Additively Manufactured Conformal Phased Arrays Utilizing 3-D Depth Sensing Camera

引用

IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2024年第11期72卷 6728-6738页

作者： Joshi, Marvin Hu, Kexin Lynch, Charles A. Cui, Yepu Soto-Valle, Genaro Tentzeris, Manos M. Georgia Inst Technol Dept Elect & Comp Engn Atlanta GA 30332 USA

The development of flexible phased array systems has drawn significant interest due to their adaptive beam steerability and deployable structures to support various platforms virtually on every conformal surface. Additively manufactured tile-based phased array offers a lightweight, flexible, and massively scalable solution with reduced cost and fabrication time. The main challenge for such conformal phased array systems is to maintain array performance under various deformations, which requires calibration of the amplitude and/or phase distribution of the antenna elements. To address this challenge, a computer vision-enabled on-the-fly adaptive shape calibration and phase correction method is proposed. The authors introduce the usage of smartphones, with integrated 3-D depth cameras and infrared (IR) sensors, and a novel computer vision algorithm to detect the bend angles between neighboring subarray tiles A $2 \ttimes 1$ additively manufactured tile-based flexible phased array is utilized as a proof-of-concept (POC) prototype to demonstrate the calibration approach. The proposed algorithm achieves a very good $<$ 1 $<^>\circ$ angular prediction accuracy and demonstrates successful calibration of the phased array under 15 $<^>\circ$ , 30 $<^>\circ$ , and 45 $<^>\circ$ bend angles under both symmetrical and asymmetrical configurations with improvements of gain as much as 7 dB. The calibrated bent phased arrays can also achieve a maximum steering range of 110 $<^>\circ$ . This approach presents a highly accurate and cost-effective calibration process that can enable massive fabrication and implementation of tile-based flexible phased arrays for next-generation 5G/mmWave wearable and conformal smart skin, Internet of Things (IoT), Industry 4.0, and massive multiple-input multiple-output (MIMO) applications.

关键词： Phased arrays Calibration Antenna arrays Sensor arrays Array signal processing Cameras Deformation Additive manufacturing calibration computer vision machine learning phased array signal processing algorithms

来源：评论

学校读者我要写书评

暂无评论

Intelligent Suppression of Interferences Based on Reinforcement Learning

引用

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第2期60卷 1400-1415页

作者： Zhang, Xiang Lan, Lan Zhu, Shengqi Li, Ximin Liao, Guisheng Xu, Jingwei Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

This article aims to investigate intelligent strategies of interference suppression for radar systems in the background of complex electromagnetic interferences. At the modeling stage, an interactive loop is established exploiting the interaction between the radar and the environment for interference suppression based on reinforcement learning. Specifically, the mappings from the interference suppression to the reinforcement learning, including the interference state set, the method set, evaluation criteria of interference suppression in different domains, and the principle of interference substate transformation, have been established. In this respect, two algorithms, including the Retroactive-Q (R-Q) learning and Retroactive-Deep Q Network (R-DQN), are developed by introducing a backtracking Q-value, which links the evaluations in each time step of a training round. At the analysis stage, the selection probabilities of the optimal implementation sequence for interference suppression are studied, and comparisons among the devised R-Q learning, R-DQN, conventional Q learning, and DQN are carried out in terms of output Q-values. Numerical results corroborate the effectiveness and robustness of the considered suppression strategies in diverse scenarios.

关键词： Interference suppression Radar Frequency modulation Time-frequency analysis Training signal processing algorithms Radar tracking Intelligent interference suppression reinforcement learning retroactive-DQN retroactive-q learning

来源：评论

学校读者我要写书评

暂无评论

High-Throughput and Flexible Belief Propagation List Decoder for Polar Codes

引用

IEEE TRANSACTIONS ON signal processing 2024年 72卷 1158-1174页

作者： Ren, Yuqing Shen, Yifei Zhang, Leyu Kristensen, Andreas Toftegaard Balatsoukas-Stimming, Alexios Boutillon, Emmanuel Burg, Andreas Zhang, Chuan Ecole Polytech Fed Lausanne EPFL TCL CH-1015 Lausanne Switzerland Southeast Univ LEADS Nanjing 210096 Peoples R China Eindhoven Univ Technol Dept Elect Engn NL-5600 MB Eindhoven Netherlands Univ Bretagne Sud CNRS UMR 6285 Lab STICC F-56100 Lorient France

Due to its high parallelism, belief propagation (BP)decoding is amenable to high-throughput applications and thusrepresents a promising solution for the ultra-high peak datarate required by future communication systems. To bridge theperformance gap compared to the widely used successive cancel-lation list (SCL) decoding algorithm, BP list (BPL) decoding forpolar codes extends candidate codeword exploration via multiplepermuted factor graphs (PFGs) to improve the error-correctingperformance of BP decoding. However, it is a significant challengeto design a unified and flexible BPL hardware architecture thatsupports various PFGs and code configurations. In this paper,we present the first VLSI implementation of a BPL decoder forpolar codes that overcomes this implementation challenge with ahardware-friendly algorithm for on-the-fly flexible ***, we introduce a sequential generation (SG) algorithm toobtain a near-optimal PFG set. Additionally, we demonstratethat any permutation can be decomposed into a combination ofmultiple fixed routings, and design a low-complexity permutationnetwork to generate graphs in an on-the-fly fashion. Our BPLdecoder has a low decoding latency by executing decoding andpermutation generation in parallel and supports arbitrary listsizes without area overhead. Experimental results based on 28nmFD-SOI technology show that for length-1024 polar codes witha code rate of one-half, our BPL decoder with 32 PFGs exhibitssimilar error-correcting performance to SCL with a list size of 4and achieves an average throughput of 25.63 Gbps and an areaefficiency of 29.46 Gbps/mm2, which is 1.82xand 4.33xfasterthan the state-of-the-art BP flip and SCL decoders, respectively

关键词： Decoding Power line communications Polar codes Codes Hardware Maximum likelihood decoding signal processing algorithms high-throughput belief propagation list (BPL) decoding factor graph permutation automorphism ensemble hardware implementation

来源：评论

学校读者我要写书评

暂无评论

Data-Reuse Adaptive algorithms for Graph signal Estimation Over Sensor Network

引用

IEEE SENSORS JOURNAL 2024年第4期24卷 5086-5096页

作者： Zhao, Haiquan Li, Chengjin Xiang, Wang Southwest Jiaotong Univ Minist Educ Key Lab Magnet Suspens Technol & Maglev Vehicle Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

The least mean square (LMS) algorithm of graph signal processing (GSP) can be used for sensor signal processing due to its simplicity and low computational complexity, and the recursive least squares (RLS) algorithm of GSP can be applied to sensor signal processing since it has fast convergence rate. However, the GSP LMS algorithm has poor convergence performance, and the tracking performance of the GSP RLS algorithm turns weak after signal mutation. To solve the mentioned problems, we focus on the data-reuse strategy, aiming to improve the convergence/tracking performance of the related algorithms by reusing the same set of data several times, and thus, the GSP data-reuse LMS (GSP-DR-LMS) algorithm and the GSP data-reuse RLSs (GSP-DR-RLSs) algorithm are proposed. Moreover, to make the GSP-DR-LMS algorithm achieve better coordination between steady-state error and convergence rate, we propose the variable step size (VSS) strategy applicable to the GSP-DR-LMS algorithm, and thus, the GSP VSS-DR LMS algorithm is proposed. In addition, the performance analysis of the related algorithms is performed. Ultimately, we verify the superiority of the proposed algorithms in terms of convergence/tracking performance by performing computer simulations.

关键词： signal processing algorithms Convergence signal processing Sensors Steady-state Wireless sensor networks Symmetric matrices Convergence tracking performance data-reuse graph signal processing (GSP) least mean square (LMS) algorithm recursive least-squares (RLS) algorithm sensor network variable step size (VSS) wireless sensor network (WSN)

来源：评论

学校读者我要写书评

暂无评论

An Integrated Approach for Vehicle State Estimation Under Non-Ideal Conditions Using Adaptive Strong Tracking Maximum Correntropy Criterion EKF

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第10期73卷 14604-14616页

作者： Bai, Shuo Hu, Jingyu Yan, Yongjun Shen, Lilin He, Zhangcheng Yin, Guodong Southeast Univ Sch Mech Engn Nanjing 211189 Peoples R China

Accurate acquisition of critical vehicle states is a prerequisite for active safety systems to work properly. However, vehicle sates under non-ideal conditions are usually difficult to be measured directly due to the high cost of sensors. To deal with the problem, an adaptive strong tracking maximum correntropy criterion extended Kalman filter (ASTMCC-EKF) is put forward to estimate vehicle states. Maximum correntropy criterion (MCC) is introduced as the optimization criterion to construct the cost function. Strong tracking filter is employed to dynamically adjust the prior error covariance matrix. Moreover, Sage-Husa suboptimal unbiased estimator is adopted to estimate the noise in real time. Simulation experiments and road tests show that ASTMCC-EKF has a more excellent estimation performance than existing algorithms under non-ideal conditions. The algorithm not only effectively suppresses the interference of non-Gaussian noise but also improves the robustness of ASTMCC-EKF under the model uncertainty and time-varying noise. Furthermore, the proposed ASTMCC-EKF shows a strong robustness to different driving conditions and road conditions.

关键词： Estimation Noise Mathematical models Wheels Vectors signal processing algorithms Kalman filters Vehicle state estimation maximum correntropy criterion strong tracking filter noise estimator

来源：评论

学校读者我要写书评

暂无评论

Leveraging Digital Cousins for Ensemble Q-Learning in Large-Scale Wireless Networks

引用

IEEE TRANSACTIONS ON signal processing 2024年 72卷 1114-1129页

作者： Bozkus, Talha Mitra, Urbashi Ming Hsieh Dept Elect & Comp Engn Univ Southern Calif Los Angeles CA 90007 USA

Optimizing large-scale wireless networks, including optimal resource management, power allocation, and throughput maximization, is inherently challenging due to their non-observable system dynamics and heterogeneous and complex nature. Herein, a novel ensemble $Q$-learning algorithm that addresses the performance and complexity challenges of the traditional $Q$-learning algorithm for optimizing wireless networks is presented. Ensemble learning with synthetic Markov Decision Processes is tailored to wireless networks via new models for approximating large state-space observable wireless networks. In particular, digital cousins are proposed as an extension of the traditional digital twin concept wherein multiple $Q$-learning algorithms on multiple synthetic Markovian environments are run in parallel and their outputs are fused into a single $Q$-function. Convergence analyses of key statistics and $Q$-functions and derivations of upper bounds on the estimation bias and variance are provided. Numerical results across a variety of real-world wireless networks show that the proposed algorithm can achieve up to 50% less average policy error with up to 40% less runtime complexity than the state-of-the-art reinforcement learning algorithms. It is also shown that theoretical results properly predict trends in the experimental results.

关键词： Wireless networks Q-learning Digital twins Complexity theory signal processing algorithms Optimization Heuristic algorithms Reinforcement learning Markov decision processes wireless networks

来源：评论

学校读者我要写书评

暂无评论

Diffusion Models for Audio Restoration

引用

IEEE signal processing MAGAZINE 2024年第6期41卷 72-84页

作者： Lemercier, Jean-Marie Richter, Julius Welker, Simon Moliner, Eloi Vaelimaeki, Vesa Gerkmann, Timo Univ Hamburg Hamburg Germany Aalto Univ Acoust Lab Espoo Finland Aalto Univ Espoo Finland

With the development of audio playback devices and fast data transmission, the demand for high sound quality is rising for both entertainment and communications. In this quest for better sound quality, challenges emerge from distortions and interferences originating at the recording side or caused by an imperfect transmission pipeline. To address this problem, audio restoration methods aim to recover clean sound signals from the corrupted input data. We present here audio restoration algorithms based on diffusion models, with a focus on speech enhancement and music restoration tasks. Traditional approaches, often grounded in handcrafted rules and statistical heuristics, have shaped our understanding of audio signals. In the past decades, there has been a notable shift toward data-driven methods that exploit the modeling capabilities of deep neural networks (DNNs). Deep generative models, and among them diffusion models, have emerged as powerful techniques for learning complex data distributions. However, relying solely on DNN-based learning approaches carries the risk of reducing interpretability, particularly when employing end-to-end models. Nonetheless, data-driven approaches allow more flexibility in comparison to statistical model-based frameworks, whose performance depends on distributional and statistical assumptions that can be difficult to guarantee. Here, we aim to show that diffusion models can combine the best of both worlds and offer the opportunity to design audio restoration algorithms with a good degree of interpretability and a remarkable performance in terms of sound quality. In this article, we review the use of diffusion models for audio restoration. We explain the diffusion formalism and its application to the conditional generation of clean audio signals. We believe that diffusion models open an exciting field of research with the potential to spawn new audio restoration algorithms that are natural-sounding and remain robust in difficult acoust

关键词： Inverse problems Acoustic noise signal processing algorithms Stochastic processes Speech enhancement Diffusion models Robustness Trajectory Machine listening

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：