检索结果-内蒙古大学图书馆

Multistage Least Squares algorithms for Clutter Suppression in Airborne Passive Radar Based on Subband Operation

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第2期59卷 1893-1909页

作者： Sui, Jinxin Wang, Jun Zuo, Luo Gao, Jie Xidian Univ Sch Elect Engn Natl Key Lab Radar Signal Proc Xian 710000 Peoples R China

This article, first, analyzes the influence of utilizing the space-time adaptive processing approaches in airborne passive radar (APR) systems on the dimension of the clutter subspace, and the signal-plus-noise ratio loss of the target, and illustrates that it is necessary to propose clutter cancellation algorithms suitable for APR systems. Combined with the property of APR systems, the multistage least squares algorithm based on subband operation (MLS-S) and the MLS algorithm based on subband operation in temporal domain (MLS-ST) are proposed for the Doppler-shift clutter cancellation. The first method, in the case of the clutter with integer Doppler-shift bins, proves to be a simple solution. The second approach, at the expense of an additional stage, is suitable for more situations. Due to the use of Doppler-angle-dependence and subband operation to coarsen the range resolution, the proposed methods not only suppress effectively clutter along the clutter ridge, but also cancel the fractional-order-range clutter. In addition, the computational complexity of the proposed algorithms is low, and the clutter cancellation operations can be performed in parallel, which provide the possibility of real-time clutter suppression for APR systems. The feasibility of the proposed approaches is verified by diverse simulated scenarios.

关键词： Clutter signal processing algorithms Doppler effect Heuristic algorithms Time-frequency analysis Passive radar Delays Airborne passive radar fractional-order-range clutter multistage least squares subband operation

来源：评论

学校读者我要写书评

暂无评论

Physics-Inspired Compressive Sensing: Beyond deep unrolling

引用

IEEE signal processing MAGAZINE 2023年第1期40卷 58-72页

作者： Zhang, Jian Chen, Bin Xiong, Ruiqin Zhang, Yongbing Peking Univ Shenzhen Grad Sch Shenzhen 518055 Peoples R China Peking Univ Comp Applicat Technol Shenzhen Grad Sch Shenzhen 518055 Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China Harbin Inst Technol Shenzhen Comp Sci & Technol Shenzhen 518055 Peoples R China

As an emerging paradigm for signal acquisition and reconstruction, compressive sensing (CS) achieves high-speed sampling and compression jointly and has found its way into many applications. With the fast growth of deep learning in computer vision, various methods of applying neural networks (NNs) in CS imaging tasks have been proposed. One category of them, named the deep unrolling network, is inspired by the physical sampling model and combines the merits of both optimization model- and data-driven methods, becoming the mainstream of this realm. In this review article, we first review the inverse imaging model and optimization algorithms encountered in the CS research and then provide the recent representative developments of CS networks, which are grouped into deep physics-free and physics-inspired approaches with respect to the utilization of sampling matrix and measurement information. Following this, we analyze the conceptual connections and relationships among various existing methods and present our perspectives on recent advances and trends for future research.

关键词： Image coding Computational modeling signal processing algorithms Transforms Market research Image restoration Task analysis

来源：评论

学校读者我要写书评

暂无评论

MIML-GAN: A GAN-Based Algorithm for Multi-Instance Multi-Label Learning on Overlapping signal Waveform Recognition

引用

IEEE TRANSACTIONS ON signal processing 2023年 71卷 859-872页

作者： Pan, Zesi Wang, Bo Zhang, Ruibin Wang, Shafei Li, Yunjie Li, Yan Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China

Existing studies for automatic waveform recognition of overlapping signals have mostly been conducted in a supervised manner. Although demonstrating superior performance in recent years, supervised methods rely heavily on sufficient labeled samples, but the acquisition of annotated data is expensive, time-consuming, and sometimes infeasible. This shortage drives the need for semi-supervised learning methods, where the unlabeled samples can be fully exploited in the training stage. In addition, multi-instance multi-label (MIML) learning is essentially another weakly supervised learning protocol, and precisely fits the form of the time-frequency images TFIs obtained from the transformation of overlapping signals. In this paper, delving into the MIML learning problem, we leverage the advantage of adversarial training to formulate an effective algorithm MIML-GAN, which is tailored to the MIML problem of overlapping signal waveform recognition. After feeding the TFIs into the network, MIML-GAN approximates the distribution of the training data using the adversarial learning principle. Subsequently, the bag-level prediction can be derived from the instance-level prediction upon the MIML discriminator through adaptive threshold calibration. Specifically, we elaborately studied the global optimality of the MIML-GAN objective function, and extensive simulations are carried out with overlapping signal dataset, validating the ascendancy of the proposed method. Comparative experiments demonstrate that the proposed algorithm possesses promising feature representation capability, and outperforms the existing semi-supervised and supervised signal waveform recognition approaches.

关键词： Task analysis Training Supervised learning signal processing algorithms Generators Generative adversarial networks Feature extraction Automatic waveform recognition overlapping radar signals residual attention learning generative adversarial network multi-instance multi-label learning

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach for Unit-Modulus Least-Squares Optimization Problems

引用

IEEE signal processing LETTERS 2023年 30卷 224-228页

作者： Rusu, Cristian Gonzalez-Prelcic, Nuria Univ Bucharest Fac Math & Comp Sci Res Ctr Log Optimizat & Secur LOS Dept Comp Sci Bucharest 050663 Romania North Carolina State Univ Raleigh NC 27606 USA

We describe a novel constrained least-squares (LS) optimization problem and propose an iterative solution that deals with the constraints of placing variables on the complex unit circle. We reformulate the LS problem with unit magnitude constraints so that we obtain efficient closed-form local updates based on Procrustes orthogonal solutions that monotonically improve the objective function. We show the performance of the proposed algorithm in a synthetic experiment and an application to hybrid precoding/combining in mmWave MIMO communications.

关键词： Optimization Iterative methods Millimeter wave communication Linear programming signal processing algorithms Radio frequency Precoding Manifold optimization unit modulus constant magnitude hybrid precoding and combining

来源：评论

学校读者我要写书评

暂无评论

Multiple Nystrom Kernel Adaptive Filter Under Minimum Generalized Cauchy Loss Criterion

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第4期70卷 1655-1659页

作者： Tang, Shenjie Wang, Haojie Li, Xifeng Bi, Dongjie Peng, Libiao Li, Zhenggui Xie, Yongle Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Sichuan Peoples R China Xihua Univ Key Lab Fluid & Power Machinery Chengdu 610039 Sichuan Peoples R China

The multikernel adaptive filters (MKAFs) have been successfully applied to resolve the issue of kernel parameter selection in traditional single kernel adaptive filters. However, owing to the linear growing network structures, conventional MKAFs commonly suffer a lot from large computational and memory burdens. To solve this problem, a multiple Nystrom approximation is proposed to curb the computational complexity of MKAFs in this brief. More concretely, the multiple Nystrom method is incorporated into the kernel generalized Cauchy conjugate gradient algorithm, generating a novel multiple Nystrom kernel generalized Cauchy conjugate gradient algorithm (MNKGCCG). It is noted that the MNKGCCG can achieve the desirable filtering performance with low computational cost in the fixed-dimensional feature space. Experimental results on Mackey-Glass time series and sunspots time series predictions in non-Gaussian noise environments demonstrate the superiorities of the proposed MNKGCCG algorithm in terms of filtering accuracy and robustness.

关键词： Kernel Adaptive filters signal processing algorithms Time series analysis Filtering Prediction algorithms Eigenvalues and eigenfunctions Kernel adaptive filter multikernel method reproducing kernel Hilbert space Nystrom method generalized Cauchy loss

来源：评论

学校读者我要写书评

暂无评论

List Encoding of Vector Perturbation Precoding

引用

IEEE signal processing LETTERS 2023年 30卷 478-482页

作者： Wang, Ke Feng, Baorui Ma, Ying Lin, Wenliang Zhao, Jingui Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Beijing 100876 Peoples R China

In this letter, we propose a new nonlinear precoding algorithm named List encoding of Vector Perturbation precoding (LVP) for Multiple Input Multiple Output (MIMO) system. Different from the traditional VP based on sphere search strategy to minimize the Frobeniuas norm of the precoded signal with a exponential search complexity, LVP adopts multiple encoders design, a list of encoder perform only one dimensional integer optimization through several iterations in parallel. During each iteration each encoder searches through several perturbation results and retain one of them to the next iteration. When the iterations ends, the historical optimal perturbation result with the least Frobenius norm is output as the precoding result. The search complexity of LVP is O(M) which increases linearly with the antenna number M. This complexity is much smaller than sphere search based strategy. Compared with other low dimensional search algorithm represented by D2VP, the search complexity drops from O(M-2) to O(M), with a significant BER gain thanks to the multiple encoder design. To the best of our knowledge, with similar BER performance the LVP algorithm has the lowest complexity.

关键词： Perturbation methods Precoding Optimization signal processing algorithms Search problems Receiving antennas MIMO communication Closest point problem MIMO nonlinear precoding VP

来源：评论

学校读者我要写书评

暂无评论

Sparsity-Constrained Kernel Recursive Generalized Maximum Versoria Criterion Algorithm

引用

IEEE signal processing LETTERS 2023年 30卷 498-502页

作者： Jain, Sandesh Majhi, Sudhan Atal Bihari Vajpayee Indian Inst Informat Technol Dept Elect & Elect Engn Gwalior 474015 India Indian Inst Sci Dept Elect Commun Engn Bangalore 560012 India

This letter proposes a novel random Fourier features (RFF) based sparsity-aware Kernel recursive adaptive filtering algorithm which employs generalized maximum Versoria criterion (GMVC) as an adaptation cost and generalized Versoria zero attraction (GVZA) as a sparsifying-norm. The proposed RFF-based GVZA kernel recursive GMVC (RFF-GVZA-KRGMVC) algorithm is robust for nonlinear sparse channel estimation under non-Gaussian noise over both stationary and non-stationary environments. Simulations indicate that the proposed RFF-GVZA-KRGMVC approach delivers better convergence and bit error rate performance over the existing stochastic gradient based RFF-ZA-kernel-MVC (RFF-ZA-KMVC) algorithm at the cost of increased computational complexity. Furthermore, detailed convergence analysis is also performed for the proposed algorithm and corroborated by Monte-Carlo simulations.

关键词： signal processing algorithms Kernel Convergence Machine learning algorithms Channel estimation Computational complexity Covariance matrices Sparsity-constrained RKHS zero-attracting maximum Versoria criterion sparse channel estimation

来源：评论

学校读者我要写书评

暂无评论

Design of 8 bit Arithmetic Logic Unit for signal processing Applications using Verilog

Design of 8 bit Arithmetic Logic Unit for Signal Processing ...

引用

Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Ujwala Ghodeswar Ashwini Gajbhiye Department of Electronics Engineering Yeshwantrao Chavan College of Engineering Nagpur India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

This work built an 8-bit arithmetic logic unit. The arithmetic logic unit is the fundamental building block of every signal processing operation. ALU is designed for performing 16 operations including addition, subtraction, multiplication, logical, bitwise, shifting, increment and decrement. Three inputs, four select lines are given to ALU as inputs and output is obtained. This ALU is implemented in Vivado 2024 software using Verilog language and FPGA target device selected is xc7a100tcsg324-1. Two methods are used to implement ALU. ALU is implemented using behavioral and structural level modelling style. Synthesis results of both the methods are calculated.

关键词： Pervasive computing Performance evaluation Power demand Social networking (online) signal processing algorithms signal processing Software Logic Hardware design languages Arithmetic

来源：评论

学校读者我要写书评

暂无评论

A Novel PAPR Reduction Scheme Based on Joint Traditional Algorithm and Machine Learning for CO-OFDM Systems

引用

IEEE PHOTONICS TECHNOLOGY LETTERS 2023年第8期35卷 418-421页

作者： Zhang, Tianhao Tong, Zhengrong Zhang, Weihua Wang, Hao Li, Peng Tianjin Univ Technol Sch Integrated Circuit Sci & Engn Tianjin 300384 Peoples R China

The high peak-to-average power ratio (PAPR) deteriorates the performance of coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. Machine learning (ML) has evolved into a powerful technology to reduce PAPR with its flexible learning ability. We propose a joint scheme which adopts iterative partial transmission sequence (IPTS) scheme cascading a feedforward neural network (FNN) trained on iterative mu-law companding and filtering (IMCF) algorithm. The proposed scheme greatly reduces the complexity of partial transmission sequence (PTS) scheme, which has excellent PAPR reduction effect and BER performance. Our method achieves a 5.03 dB PAPR reduction for 10(-4) CCD F and an additional transmission distance of 200 km SMF for 10(-3) BER, compared with the original OFDM signal. Meanwhile, the computational complexity of proposed scheme is reduced by 32.4%, compared with IPTS cascading IMCF (IPTS-IMCF) scheme.

关键词： Peak to average power ratio Training Optical transmitters signal processing algorithms Passive optical networks Filtering Complexity theory Coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems peak-to-average power ratio (PAPR) reduction machine learning (ML) iterative partial transmission sequence (IPTS) feedforward neural network (FNN) iterative mu-law companding and filtering (IMCF)

来源：评论

学校读者我要写书评

暂无评论

Cross-Receiver Radio Frequency Fingerprint Identification Based on Contrastive Learning and Subdomain Adaptation

引用

IEEE signal processing LETTERS 2023年 30卷 70-74页

作者： Zha, Xiong Li, Tianyun Qiu, Zhaoyang Li, Fang Informat Engn Univ Sch Informat Engn Zhengzhou 450000 Peoples R China

Radio frequency fingerprint (RFF) identification is emerging as an attractive paradigm for physical layer security. Despite the exceptional accuracy achieved by deep learning (DL) based schemes, few works consider the cross-receiver scenario. The performance deteriorates significantly when the model is deployed on new receivers directly. To this end, a cross-receiver RFF learning scheme is proposed. First, an unsupervised pre-training method based on contrastive learning is utilized to extract receiver-agnostic features. Then, the model is optimized by subdomain adaptation to further improve identification performance. The proposed scheme does not require multiple labeled datasets from different receivers. And experimental results indicate that the proposed scheme effectively alleviates performance degradation in the cross-receiver scenario.

关键词： Receivers Feature extraction Adaptation models signal to noise ratio Training signal processing algorithms Degradation Radio frequency fingerprint cross-receiver contrastive learning subdomain adaptation

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：