用于宽带时空自适应处理的 CQCNN-SV 算法

IF 1.7 4区 工程技术 Q2 COMPUTER SCIENCE, THEORY & METHODS Multidimensional Systems and Signal Processing Pub Date : 2024-08-29 DOI:10.1007/s11045-024-00892-4
Ruiyan Du, Xiaodan Chen, Guangyu Meng, Liwen Feng, Yajie Gao, Fulai Liu
{"title":"用于宽带时空自适应处理的 CQCNN-SV 算法","authors":"Ruiyan Du, Xiaodan Chen, Guangyu Meng, Liwen Feng, Yajie Gao, Fulai Liu","doi":"10.1007/s11045-024-00892-4","DOIUrl":null,"url":null,"abstract":"<p>This paper presents a wideband robust beamforming algorithm based on a complex quantized convolutional neural network (CQCNN) for solving the steering vector (SV) mismatch problem, named as CQCNN-SV algorithm. Firstly, the CQCNN is constructed by the complex convolution layers, quantization assistance layers, and normalization layers, respectively. Specially, the network channel filtering threshold function is used to construct the quantization assistance layer with the functions of network weight pruning. The CQCNN structure is suitable for wideband beamforming in space–time two-dimensional signal processing, which can improve the feature extraction ability and convergence speed of complex-valued data. Subsequently, the mismatched desired signal SV is corrected by solving the quadratic programming problem, and the corrected SV is treated as the training label. Finally, the space–time two-dimensional covariance matrix and the training label are fed into the CQCNN model. The wideband beamforming weight vector in the space–time antenna structure is given by the desired signal SV, which is predicted by the well-trained CQCNN. Theoretical analysis and simulation experiments show that the proposed algorithm not only has good real-time performance but also has stable system output performance.\n</p>","PeriodicalId":19030,"journal":{"name":"Multidimensional Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CQCNN-SV algorithm for wideband space–time adaptive processing\",\"authors\":\"Ruiyan Du, Xiaodan Chen, Guangyu Meng, Liwen Feng, Yajie Gao, Fulai Liu\",\"doi\":\"10.1007/s11045-024-00892-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This paper presents a wideband robust beamforming algorithm based on a complex quantized convolutional neural network (CQCNN) for solving the steering vector (SV) mismatch problem, named as CQCNN-SV algorithm. Firstly, the CQCNN is constructed by the complex convolution layers, quantization assistance layers, and normalization layers, respectively. Specially, the network channel filtering threshold function is used to construct the quantization assistance layer with the functions of network weight pruning. The CQCNN structure is suitable for wideband beamforming in space–time two-dimensional signal processing, which can improve the feature extraction ability and convergence speed of complex-valued data. Subsequently, the mismatched desired signal SV is corrected by solving the quadratic programming problem, and the corrected SV is treated as the training label. Finally, the space–time two-dimensional covariance matrix and the training label are fed into the CQCNN model. The wideband beamforming weight vector in the space–time antenna structure is given by the desired signal SV, which is predicted by the well-trained CQCNN. Theoretical analysis and simulation experiments show that the proposed algorithm not only has good real-time performance but also has stable system output performance.\\n</p>\",\"PeriodicalId\":19030,\"journal\":{\"name\":\"Multidimensional Systems and Signal Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Multidimensional Systems and Signal Processing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s11045-024-00892-4\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Multidimensional Systems and Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11045-024-00892-4","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0

摘要

本文提出了一种基于复量化卷积神经网络(CQCNN)的宽带鲁棒波束成形算法,用于解决转向矢量(SV)不匹配问题,命名为 CQCNN-SV 算法。首先,CQCNN 分别由复卷积层、量化辅助层和归一化层构成。其中,网络通道滤波阈值函数用于构建量化辅助层,并具有网络权重剪枝功能。CQCNN 结构适用于时空二维信号处理中的宽带波束成形,能提高复值数据的特征提取能力和收敛速度。随后,通过求解二次编程问题对不匹配的期望信号 SV 进行修正,并将修正后的 SV 作为训练标签。最后,将时空二维协方差矩阵和训练标签输入 CQCNN 模型。时空天线结构中的宽带波束成形权重向量由期望信号 SV 给出,而期望信号 SV 则由训练有素的 CQCNN 预测。理论分析和仿真实验表明,所提出的算法不仅具有良好的实时性,而且具有稳定的系统输出性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
CQCNN-SV algorithm for wideband space–time adaptive processing

This paper presents a wideband robust beamforming algorithm based on a complex quantized convolutional neural network (CQCNN) for solving the steering vector (SV) mismatch problem, named as CQCNN-SV algorithm. Firstly, the CQCNN is constructed by the complex convolution layers, quantization assistance layers, and normalization layers, respectively. Specially, the network channel filtering threshold function is used to construct the quantization assistance layer with the functions of network weight pruning. The CQCNN structure is suitable for wideband beamforming in space–time two-dimensional signal processing, which can improve the feature extraction ability and convergence speed of complex-valued data. Subsequently, the mismatched desired signal SV is corrected by solving the quadratic programming problem, and the corrected SV is treated as the training label. Finally, the space–time two-dimensional covariance matrix and the training label are fed into the CQCNN model. The wideband beamforming weight vector in the space–time antenna structure is given by the desired signal SV, which is predicted by the well-trained CQCNN. Theoretical analysis and simulation experiments show that the proposed algorithm not only has good real-time performance but also has stable system output performance.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Multidimensional Systems and Signal Processing
Multidimensional Systems and Signal Processing 工程技术-工程:电子与电气
CiteScore
5.60
自引率
8.00%
发文量
50
审稿时长
11.7 months
期刊介绍: Multidimensional Systems and Signal Processing publishes research and selective surveys papers ranging from the fundamentals to important new findings. The journal responds to and provides a solution to the widely scattered nature of publications in this area, offering unity of theme, reduced duplication of effort, and greatly enhanced communication among researchers and practitioners in the field. A partial list of topics addressed in the journal includes multidimensional control systems design and implementation; multidimensional stability and realization theory; prediction and filtering of multidimensional processes; Spatial-temporal signal processing; multidimensional filters and filter-banks; array signal processing; and applications of multidimensional systems and signal processing to areas such as healthcare and 3-D imaging techniques.
期刊最新文献
CQCNN-SV algorithm for wideband space–time adaptive processing An improved hybrid fusion of noisy medical images using differential evolution-based artificial rabbits optimization algorithm Compressive sensing imaging with periodic perturbation induced caustic lens masks in a ripple tank Design and implementation of power and area efficient architectures of circular symmetry 2-D FIR filters using CSOA-based CSD Novel two-dimensional Wigner distribution and ambiguity function in the framework of the two-dimensional nonseparable linear canonical transform
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1