Signal Processing最新文献_第5页

Content adaptive JND profile by leveraging HVS inspired channel modeling and perception oriented energy allocation optimization 利用受 HVS 启发的信道建模和以感知为导向的能量分配优化，实现内容自适应 JND 配置文件

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-10-15 DOI: 10.1016/j.sigpro.2024.109734

Haibing Yin , Xia Wang , Guangtao Zhai , Xiaofei Zhou , Chenggang Yan

{"title":"Content adaptive JND profile by leveraging HVS inspired channel modeling and perception oriented energy allocation optimization","authors":"Haibing Yin , Xia Wang , Guangtao Zhai , Xiaofei Zhou , Chenggang Yan","doi":"10.1016/j.sigpro.2024.109734","DOIUrl":"10.1016/j.sigpro.2024.109734","url":null,"abstract":"<div><div>The existing just noticeable difference (JND) models consider the effects of various covariates, however, they rarely account for the fusion relationship between the covariates, i.e., they lack a holistic understanding of the mechanisms of visual perception and disregarding the significant impact of energy consumption on visual perception. In fact, visual perception is no exception to the rule that nerve activities and energy supply are inextricably linked. Based on this insight, this paper proposes a novel JND estimation model employing content-adaptive energy allocation. Primarily, the information theory is applied to the visual perception system by conceptualizing human visual system (HVS) as an information communication framework. Then, leveraging the relationship between energy consumption and information perception, this paper quantitatively measures the HVS energy consumption as uniform metric to describe the complicated and heterogeneous HVS perception process, and then construct JND model by fusing low-level and Semantic-level features. Numerous simulation results verify that the proposed JND model is significantly competitive with other frontier models and highly compatible with HVS.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109734"},"PeriodicalIF":3.4,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generalized sidelobe canceller based adaptive multiple-input multiple-output radar array beamforming under scenario mismatches 场景错配下基于广义侧叶消除器的自适应多输入多输出雷达阵列波束成形

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-10-15 DOI: 10.1016/j.sigpro.2024.109739

Cheng-Jie Wang , Ju-Hong Lee

{"title":"Generalized sidelobe canceller based adaptive multiple-input multiple-output radar array beamforming under scenario mismatches","authors":"Cheng-Jie Wang , Ju-Hong Lee","doi":"10.1016/j.sigpro.2024.109739","DOIUrl":"10.1016/j.sigpro.2024.109739","url":null,"abstract":"<div><div>It is well known that the performance of an adaptive MIMO radar array fully depends on the precise steering control and is deteriorated by even a small scenario mismatch. This paper presents an advanced generalized sidelobe canceller (AGSC) based adaptive MIMO radar array beamformer with robustness against the effect due to scenario mismatches. A new signal blocking matrix is developed for effectively blocking the desired signal when the adaptive beamforming is performed under multiple scenario mismatches. The novelty of the new signal blocking matrix is that it contains two additional matrix components in addition to the conventional blocking matrix. The first one is a matrix made up of the basis orthogonal to some appropriately designed derivative constraint vector. It avoids the possible leakage of the desired signal due to scenario mismatches. The other one is a matrix made up of the dominant eigenvectors associated with the correlation matrix of the blocked data vector at the output of the first matrix component. It is employed to preserve all of the interference signals. As a result, the whole blocking operation can delete the desired signal and save the interference signals under multiple scenario mismatches. Hence, the AGSC based adaptive MIMO radar beamformer effectively deals with the performance degradation caused by scenario mismatches without resorting to any robust optimization algorithms. Performance analysis and complexity evaluation regarding the AGSC based adaptive MIMO radar beamformer are presented. Simulation results are also provided for confirmation and comparison.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109739"},"PeriodicalIF":3.4,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interacting multiple model adaptive robust Kalman filter for process and measurement modeling errors simultaneously 同时处理过程和测量建模误差的交互式多模型自适应鲁棒卡尔曼滤波器

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-10-15 DOI: 10.1016/j.sigpro.2024.109743

Baojian Yang, Huaiguang Wang, Zhiyong Shi

引用次数: 0

Learning bipartite graphs from spectral templates 从光谱模板中学习二方图

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-10-11 DOI: 10.1016/j.sigpro.2024.109732

Subbareddy Batreddy , Aditya Siripuram , Jingxin Zhang

引用次数: 0

Optimizing beamforming in quaternion signal processing using projected gradient descent algorithm 利用投影梯度下降算法优化四元数信号处理中的波束成形

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-10-11 DOI: 10.1016/j.sigpro.2024.109738

Qiankun Diao , Dongpo Xu , Shuning Sun , Danilo P. Mandic

引用次数: 0

A novel synchrosqueezing transform associated with linear canonical transform 与线性典型变换相关的新型同步queezing变换

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-10-11 DOI: 10.1016/j.sigpro.2024.109733

Hongxia Miao

{"title":"A novel synchrosqueezing transform associated with linear canonical transform","authors":"Hongxia Miao","doi":"10.1016/j.sigpro.2024.109733","DOIUrl":"10.1016/j.sigpro.2024.109733","url":null,"abstract":"<div><div>Synchrosqueezing transforms have aroused great attention for its ability in time–frequency energy rearranging and signal reconstruction, which are post-processing techniques of the time–frequency distribution. However, the time–frequency distributions, such as short-time Fourier transform and short-time fractional Fourier transform, cannot change the shape of the time–frequency distribution. The linear canonical transform (LCT) can simultaneously rotate and scale the time–frequency distribution, which enlarges the distance between different signal components with proper parameters. In this study, a convolution-type short-time LCT is proposed to present the time–frequency distribution of a signal, from which the signal reconstruction formula is given. Its resolutions in time and LCT domains are demonstrated, which helps to select suitable window functions. A fast implementation algorithm for the short-time LCT is provided. Further, the synchrosqueezing LCT (SLCT) transform is designed by performing synchrosqueezing technique on the short-time LCT. The SLCT inherits many properties of the LCT, and the signal reconstruction formula is obtained from the SLCT. Adaptive selections of the parameter matrix of LCT and the length of the window function are introduced, thereby enabling proper compress direction and resolution of the signal. Finally, numerical experiments are presented to verify the efficiency of the SLCT.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109733"},"PeriodicalIF":3.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Video reversible data hiding: An evolution to local distortion-tolerance framework 视频可逆数据隐藏：向局部失真容限框架演进

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-10-11 DOI: 10.1016/j.sigpro.2024.109730

Jiaqi Wang, Bo Ou

引用次数: 0

EKF-based parameter estimation method for radar maneuvering target with unknown time information 基于 EKF 的未知时间信息雷达机动目标参数估计方法

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-10-11 DOI: 10.1016/j.sigpro.2024.109731

Huagui Du, Jiahua Zhu, Yongping Song, Chongyi Fan, Xiaotao Huang

{"title":"EKF-based parameter estimation method for radar maneuvering target with unknown time information","authors":"Huagui Du, Jiahua Zhu, Yongping Song, Chongyi Fan, Xiaotao Huang","doi":"10.1016/j.sigpro.2024.109731","DOIUrl":"10.1016/j.sigpro.2024.109731","url":null,"abstract":"<div><div>Moving target detection (MTD) is a research hotspot in radar signal processing. Generally, the time information of non-cooperative moving targets entering and leaving a radar coverage area is unknown, which would lead to severe performance loss for target parameter estimation, detection, and imaging. Unlike our previous research work, this paper addresses the motion parameters estimation and refocusing problem for a radar maneuvering target with unknown entry and departure time. A computationally efficient method that utilizes extended Kalman filtering (EKF) for phase tracking is proposed to estimate the entry and departure times. The proposed method first performs range cell migration correction (RCMC) on the pulse compression echo signal. Then, the maneuvering target signal is modeled as a polynomial phase signal (PPS) and utilizes the EKF to construct a binary state-space equation for polynomial phase tracking. Finally, by comparing the phase tracking results of the noise cell and the signal cell, one can derive estimates for the entry/departure time and motion parameters. Compared with existing methods, the proposed method avoids multi-dimension searching on the parameter space, so it has a prominent advantage in computational complexity. Moreover, the core of the proposed method lies in tracking the polynomial phase, which is not constrained by the order of target motion, and has wider applicability in practice. Both simulated and public radar data are used to validate the effectiveness of the proposed method.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109731"},"PeriodicalIF":3.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A fast block sparse Kaczmarz algorithm for sparse signal recovery 用于稀疏信号恢复的快速块稀疏 Kaczmarz 算法

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-10-11 DOI: 10.1016/j.sigpro.2024.109736

Yu-Qi Niu, Bing Zheng

引用次数: 0

One-step multi-view spectral clustering based on multi-feature similarity fusion 基于多特征相似性融合的一步式多视角光谱聚类

IF 3.4 2区工程技术

Signal Processing Pub Date : 2024-10-10 DOI: 10.1016/j.sigpro.2024.109729

Dezheng Kong , Shuisheng Zhou , Sheng Jin , Feng Ye , Ximin Zhang

{"title":"One-step multi-view spectral clustering based on multi-feature similarity fusion","authors":"Dezheng Kong , Shuisheng Zhou , Sheng Jin , Feng Ye , Ximin Zhang","doi":"10.1016/j.sigpro.2024.109729","DOIUrl":"10.1016/j.sigpro.2024.109729","url":null,"abstract":"<div><div>Multi-view clustering has attracted increasing attention for handling complex data with multiple views or sources. Among them, spectral clustering-based methods become more and more popular due to it can make full use of information from different views. However, most existing multi-view spectral clustering methods typically adopt a two-step scheme, which firstly obtains the spectral embedding matrix through graph fusion or multi-feature fusion, and then uses the k-means algorithm to cluster the spectral embedding matrix to obtain the final clustering result. This two-step scheme inevitably leads to information loss, resulting in a suboptimal solution. Furthermore, the methods of graph fusion and multi-feature fusion have not taken into account the inconsistency of features between different views and the unordered nature of clustering labels, which also decreases the clustering performance. To solve these problems, we propose a novel one-step multi-view spectral clustering based on multi-feature similarity fusion. This model simultaneously conducts graph learning, multi-feature similarity fusion and discretization in a unified framework, which can mutually negotiate and optimize each other to achieve better results. Furthermore, compared to directly fusing affinity matrices or spectral embedding matrixs from different views, we take advantage of the property of the spectral embedding matrix, fuse the similarity of samples in feature space, better handle the differences between different views. Finally, the superiority of our method is verified by the experimental evaluation of several data sets. The demo code of this work is publicly available at <span><span>https://github.com/kong-de-zheng/MOMSC</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109729"},"PeriodicalIF":3.4,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0