Signal Processing最新文献_第9页

On the analysis and comparison between MPR and cartesian for TDOA localization MPR与直角坐标在TDOA定位中的分析与比较

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-06-16 DOI: 10.1016/j.sigpro.2025.110148

Yimao Sun , Tianyi Xing , Yanbin Zou , Yangbing Yang , Liangyin Chen

{"title":"On the analysis and comparison between MPR and cartesian for TDOA localization","authors":"Yimao Sun , Tianyi Xing , Yanbin Zou , Yangbing Yang , Liangyin Chen","doi":"10.1016/j.sigpro.2025.110148","DOIUrl":"10.1016/j.sigpro.2025.110148","url":null,"abstract":"<div><div>Modified polar representation (MPR) provides a unified mathematical framework for both point localization and direction finding in near-field and far-field scenarios. Although prior research has shown that MPR alleviates the range thresholding effect, which refers to the sudden degradation in localization accuracy when the source moves beyond a critical distance, a rigorous theoretical explanation and comprehensive comparison with the Cartesian representation (CR) are still lacking. This paper analyzes the advantages and limitations of MPR and CR for time difference of arrival (TDOA)-based localization under both known and unknown signal propagation speeds (SPS). While the Cramér–Rao lower bound (CRLB) and hybrid Bhattacharyya–Barankin bound (HBBB) have been studied previously for known-SPS scenarios in Wang and Ho (2017), we derive and analyze the HBBB under the unknown-SPS setting. HBBB provides a tighter analytical evaluation beyond the CRLB, so it can quantify the thresholding effect when the source is distant or noise is high. Furthermore, an analytical comparison based on the Hessian of the maximum likelihood (ML) cost function is performed to reveal why MPR is more noise-robust in far-field conditions, whereas CR performs better in the near field—findings supported by the HBBB. Additionally, the far-field case is investigated, establishing the equivalence of MPR with the conventional far-field model in estimating both angles and SPS. These results enhance the theoretical understanding of MPR and underscore its practical implications for localization and sensing applications.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110148"},"PeriodicalIF":3.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366605","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

Trustworthy data recovery for incomplete multi-view learning 不完全多视图学习的可信数据恢复

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-06-16 DOI: 10.1016/j.sigpro.2025.110146

Huangyi Deng, Ningning Pan, Chuanqing Tang, Long Shi

{"title":"Trustworthy data recovery for incomplete multi-view learning","authors":"Huangyi Deng, Ningning Pan, Chuanqing Tang, Long Shi","doi":"10.1016/j.sigpro.2025.110146","DOIUrl":"10.1016/j.sigpro.2025.110146","url":null,"abstract":"<div><div>Incomplete multi-view learning has recently made progress towards more reliable decision-making. Existing methods mainly follow a two-step process: first, conducting data imputation, and then performing opinion aggregation based on evidential deep learning. Although these methods evaluate reliability in the final decision-making phase, they neglect leveraging uncertainty to guide high-quality data imputation. In this paper, we put forward a novel trusted framework termed as Trustworthy Data Recovery for Incomplete Multi-view Learning (TDR-IML) which enables trustworthy data imputation in an uncertain-supervision way. First, we obtain the <span><math><mi>k</mi></math></span>-nearest neighbor nodes of the incomplete data instance and construct a multivariate Gaussian distribution to model the missing data’s latent space. Then, we perform multiple samplings for the missing data and filter out low-quality samples whose uncertainty exceeds the average uncertainty of all the sampled data. In addition, we refine the opinion decoupling strategy to mitigate semantic ambiguity, thereby improving the extraction of both consistent and complementary opinions. We finally conduct experiments on real-world datasets to validate our model. The code is available on <span><span>https://github.com/ding6ding/TDR-IMV</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110146"},"PeriodicalIF":3.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314611","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

An active contour model based on Kullback–Leibler divergence and morphology for image segmentation with edge leakage 基于Kullback-Leibler散度和形态学的边缘泄漏图像分割主动轮廓模型

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-06-14 DOI: 10.1016/j.sigpro.2025.110143

Zhen Li, Fuzheng Zhang, Guina Wang, Guirong Weng, Yiyang Chen

{"title":"An active contour model based on Kullback–Leibler divergence and morphology for image segmentation with edge leakage","authors":"Zhen Li, Fuzheng Zhang, Guina Wang, Guirong Weng, Yiyang Chen","doi":"10.1016/j.sigpro.2025.110143","DOIUrl":"10.1016/j.sigpro.2025.110143","url":null,"abstract":"<div><div>Intensity inhomogeneity and edge leakage tend to make traditional image segmentation methods unavailable due to uneven lighting and severe noise, etc. Most active contour models (ACMs) perform ineffectively when utilized for these images with noise and intensity inhomogeneity. To relieve these issues, the robust ACM based on Kullback–Leibler divergence and mathematical morphology (KLMM) is proposed to segment images with intensity inhomogeneity. This Kullback–Leibler divergence is applied for the local energy term to differentiate intensity variation between the true distribution and fitting distribution in a local region, which integrates Retinex modeling. The fitting bias formulation is computed with morphological operators to correct intensity fluctuation variance and mitigate edge leakage caused by noise and uneven lighting. The data optimization function and the average filtering diminish accumulation errors in iteration and assure correct evolution. The experimental results on various real and synthetic images manifest the proposed model with its mIOU converging over 0.9 outperforms comprehensively several existing state-of-art models in accuracy and robustness.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110143"},"PeriodicalIF":3.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366602","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

On adaptive multichannel dereverberation based on dichotomous coordinate descent and data-reuse techniques 基于二分类坐标下降和数据复用技术的自适应多信道去噪

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-06-14 DOI: 10.1016/j.sigpro.2025.110138

Wenxing Yang , Jilu Jin , Kaili Yin , Jingdong Chen , Jacob Benesty

{"title":"On adaptive multichannel dereverberation based on dichotomous coordinate descent and data-reuse techniques","authors":"Wenxing Yang , Jilu Jin , Kaili Yin , Jingdong Chen , Jacob Benesty","doi":"10.1016/j.sigpro.2025.110138","DOIUrl":"10.1016/j.sigpro.2025.110138","url":null,"abstract":"<div><div>Multichannel linear prediction (MCLP) is widely used for speech dereverberation, with recursive least-squares (RLS)-like algorithms commonly applied to update the linear prediction coefficients. However, these algorithms tend to be computationally intensive, making it necessary in practical implementations to reduce complexity while improving numerical robustness for better dereverberation performance. In this paper, we introduce a more efficient MCLP-based adaptive dereverberation method that combines dichotomous coordinate descent (DCD) with a data-reuse (DR) technique. Compared to the traditional RLS-based approach, the proposed method offers two major benefits. First, it significantly lowers computational demands by replacing most multiplications with bitshifts during DCD iterations, making it more suitable for real-world applications. Second, by avoiding the propagation of the inverse covariance matrix via the Riccati equation, the method ensures numerical stability, making it more suitable for processing long-duration speech signals. Additionally, the DR technique improves dereverberation performance by more efficiently utilizing available observed data. Simulation results show that the proposed methods outperform the conventional RLS-based approach in terms of both numerical stability and computational efficiency, while delivering comparable dereverberation performance.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110138"},"PeriodicalIF":3.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314609","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

Micro-expression recognition through feature enhancement and region weighted fusion based on supervised contrastive learning 基于监督对比学习的特征增强和区域加权融合微表情识别

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-06-13 DOI: 10.1016/j.sigpro.2025.110171

Shuaichao Li, Mingze Li, Jiaao Sun, Shuhua Lu

{"title":"Micro-expression recognition through feature enhancement and region weighted fusion based on supervised contrastive learning","authors":"Shuaichao Li, Mingze Li, Jiaao Sun, Shuhua Lu","doi":"10.1016/j.sigpro.2025.110171","DOIUrl":"10.1016/j.sigpro.2025.110171","url":null,"abstract":"<div><div>Micro-expression recognition has aroused active research interest due to its extensive applications in various fields including public security, human-computer interaction, medical care etc. However, micro-expression suffers from extremely low intensity and short duration, resulting in enormous difficulty in its accurate identification. In this article, to improve the feature correlation of homogeneous samples and enhance the ability of local detailed feature extraction, a feature enhancement and regional weighted fusion method for micro-expression recognition based on supervised contrast learning has been proposed. Specifically, using ResNet as backbone, a powerful dual branch network under supervised contrast learning is designed, which on the one hand extracts the features of the eye and mouth regions respectively, and on the other hand improves the feature correlation of the homogeneous sample pair. Among of them, motion amplification and optical flow are used to amplify the subtle facial features to improve their discrimination. To effectively perceive the vital fine-grained feature information, a SE-Conv refinement attention mechanism is proposed to suppress background interference and a region weighted fusion strategy is adopted to combine features from different facial regions. The proposed method has been evaluated extensively on three public datasets, outperforming most of state-of-the-art methods.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110171"},"PeriodicalIF":3.4,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314610","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

Neural predictor aided policy optimization for adversarial controlled sensing 对抗控制传感的神经预测辅助策略优化

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-06-13 DOI: 10.1016/j.sigpro.2025.110115

Nicholas Kalouptsidis, George Stamatelis

{"title":"Neural predictor aided policy optimization for adversarial controlled sensing","authors":"Nicholas Kalouptsidis, George Stamatelis","doi":"10.1016/j.sigpro.2025.110115","DOIUrl":"10.1016/j.sigpro.2025.110115","url":null,"abstract":"<div><div>This paper is concerned with the fundamental problem of controlled sensing, namely how to optimize signal processing resources in a sensor network in order to detect the true hidden state of the environment, when the sensors are subject to adversarial attacks. The sensing task is performed by a legitimate agent who actively selects observations generated by a set of sensors and makes inference about the true state by minimizing the error probability. The adversary may have access to all or a subset of the sensors and can influence the quality of observations. Agents may have only partial access to the complete data set, leading to different beliefs about the true state, different perceptions of the error probability. To address the complexities of this problem, we define well motivated approximate structures that fill the gap of partial information. We provide three different objective functions for training a neural predictor, and we demonstrate how prediction quality is a precondition for detection performance. Based on the above concepts, we propose a novel deep reinforcement learning (DRL) algorithm, termed Predictive Proximal Policy Optimization for Adversarial Controlled Sensing (3POACS) algorithm. This algorithm combines building blocks from single agent DRL, problem specific reward reshaping procedures, and a neural predictor. Finally, we use an anomaly detection example to demonstrate the superiority of the proposed method over previous non-adversarial approaches. Experiments show that the new algorithm favorably competes with DRL algorithms with access to oracle predictors.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110115"},"PeriodicalIF":3.4,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288920","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

Tensor completion via Tucker decomposition with Correlated Total Variation regularization on factor matrices 基于因子矩阵相关总变分正则化的Tucker分解张量补全

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-06-12 DOI: 10.1016/j.sigpro.2025.110139

Min Wang , Zhuying Chen , Qiang Wu , Liang Zhong

{"title":"Tensor completion via Tucker decomposition with Correlated Total Variation regularization on factor matrices","authors":"Min Wang , Zhuying Chen , Qiang Wu , Liang Zhong","doi":"10.1016/j.sigpro.2025.110139","DOIUrl":"10.1016/j.sigpro.2025.110139","url":null,"abstract":"<div><div>Multidimensional data, such as color images and videos, often exhibit inherent low-rank and local smoothness properties, with temporal and spatial correlations playing a crucial role in data recovery. While most existing methods focus on modeling these properties independently, they often overlook their coupled correlation in the factor space derived during tensor decomposition. In this study, we propose a novel Matrix Correlated Total Variation (MCTV) regularizer to explicitly model the coupled correlation between low-rankness and smoothness within Tucker decomposition. Unlike traditional methods, MCTV propagates the low-rankness of factor matrices to the Tucker rank, eliminating the need to predefine the core tensor size. It also preserves temporal and spatial smoothness correlations across all tensor modes through operations on smooth factor matrices. By integrating MCTV into a Tucker-based tensor completion model, we remove the dependence on hyperparameters like the Tucker rank and tradeoff parameters, creating a unified framework for capturing these coupled correlations. To optimize the proposed model, we design an efficient Alternating Direction Method of Multipliers (ADMM) algorithm. Experimental results on benchmark datasets demonstrate the superiority of our method in recovering multidimensional data by effectively modeling the synergy between low-rankness and smoothness.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110139"},"PeriodicalIF":3.4,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307858","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

Open set recognition of radar specific emitter based on adversarial reciprocal point learning 基于对抗性倒易点学习的雷达特定辐射源开集识别

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-06-11 DOI: 10.1016/j.sigpro.2025.110137

Wenxu Zhang , Lin An , Wencheng Yang , Zhongkai Zhao , Feiran Liu

{"title":"Open set recognition of radar specific emitter based on adversarial reciprocal point learning","authors":"Wenxu Zhang , Lin An , Wencheng Yang , Zhongkai Zhao , Feiran Liu","doi":"10.1016/j.sigpro.2025.110137","DOIUrl":"10.1016/j.sigpro.2025.110137","url":null,"abstract":"<div><div>Radar specific emitter identification (SEI) is a key technology in electromagnetic spectrum control. Although the emergence of deep learning has promoted the development of SEI, there are still many shortcomings in the current research results. Most of the traditional deep learning algorithms are applicable to closed-set identification and can only be used when the database is complete. In addition, individual differences in radar signals are susceptible to noise interference, but traditional denoising methods are usually independent of the feature extraction process, making it difficult to ensure that certain individual information is not lost. Therefore, in this paper, we propose a new radar emitter open set recognition method called adversarial reciprocal point learning with adaptive denoising (ARPLAD). Firstly, we design a new feature extraction network for one-dimensional signals, which combines deep residual shrinkage network with efficient attention mechanism to autonomously denoise signals and focus on important parts of signal features. Secondly, we train the network using adversarial reciprocal point learning combined with center loss to extract discriminative features with compact intraclass distances and separable interclass distances, which can efficiently discriminate unknown signals and reduce the risk of open set identification. The experimental results show that ARPLAD exhibits excellent performance in different conditions, providing an effective solution for SEI in open electromagnetic environments.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110137"},"PeriodicalIF":3.4,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279266","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

Underwater multi-sensor multi-target bearing-only tracking based on delayed measurements 基于延迟测量的水下多传感器多目标单方位跟踪

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-06-10 DOI: 10.1016/j.sigpro.2025.110134

Shuo Li, Nan Zou, Bin Qi, Guolong Liang, Xiang Li

{"title":"Underwater multi-sensor multi-target bearing-only tracking based on delayed measurements","authors":"Shuo Li, Nan Zou, Bin Qi, Guolong Liang, Xiang Li","doi":"10.1016/j.sigpro.2025.110134","DOIUrl":"10.1016/j.sigpro.2025.110134","url":null,"abstract":"<div><div>The multi-sensor bearing-only tracking system plays an important role in underwater target localization. However, due to propagation delays, observations from multiple sensors at the same time are asynchronous, as the signals are emitted from the target at different moments, creating a time offset issue. Additionally, as the relative distance between the target and sensors changes, the time intervals between two consecutive observations by the sensors also vary. Furthermore, missed detections, false alarms, and target birth and death phenomena negatively impact state estimation. To address these issues, this paper proposes a multi-sensor bearing-only tracking algorithm that considers propagation delay. Using the Gauss–Helmert model to achieve accurate state transitions, the algorithm establishes kinematic and temporal constraints between multi-sensors. Simultaneously, it employs a loopy belief propagation method to calculate the posterior probabilities of each target and perform fusion updates. To improve tracking accuracy in complex environments, a time decay factor is proposed to reduce the covariance of sensors with higher delays, and a trajectory birth-and-death management strategy combined with delayed decisions is proposed. Simulation results show that this algorithm outperforms traditional algorithms that ignore propagation delays in state estimation accuracy and perform better than existing bias compensation algorithms in environments with false alarms and missed detections.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110134"},"PeriodicalIF":3.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314608","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

Distributed heterogenous multi-sensor fusion based on labeled random finite set for multi-target state estimation using information geometry 基于标记随机有限集的异构多传感器融合信息几何多目标状态估计

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-06-09 DOI: 10.1016/j.sigpro.2025.110135

Chenghu Cao , Yongbo Zhao

{"title":"Distributed heterogenous multi-sensor fusion based on labeled random finite set for multi-target state estimation using information geometry","authors":"Chenghu Cao , Yongbo Zhao","doi":"10.1016/j.sigpro.2025.110135","DOIUrl":"10.1016/j.sigpro.2025.110135","url":null,"abstract":"<div><div>This paper considers the challenging problem of heterogenous multi-sensor fusion using information geometry theory. A novel distributed heterogenous fusion method with labeled random finite set (RFS) multi-target densities is proposed to address multi-sensor multi-target tracking problem. The Fisher information distance (FID) is used to characterize the information acquisition and sensing capability of distributed sensor nodes by analyzing measurement model of sensor nodes via geodesic computation on statistical manifold. A scalar fusion weight is assigned to each local multi-target density in order to characterize its relative information confidence under the different architecture of sensor nodes. The fusion weights are adjusted according to the contribution of each sensor node to global multi-target density. Thus, the proposed distributed heterogenous fusion serves as an adaptive algorithm for multi-sensor multi-target tracking within the monitored area. Aiming to achieve multi-target tracking under the limited sensing capabilities of sensor nodes, we firstly formulate the general framework of distributed heterogenous fusion strategies based on sensor information accumulation. Secondly, we present both numerical and approximate solutions to computing the geodesic on the manifold for FID calculations, furtherly for determining locally tailored fusion weights for different sensor nodes. Finally, we present several simulation results to validate the efficacy of our proposed distributed heterogenous fusion algorithm for multi-target tracking. These simulations involve three typical types of sensor nodes where each sensor agent performs a multi-scan generalized labeled multi-Bernoulli (GLMB) smoothing algorithm, demonstrating superior performance in multi-target state estimation.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110135"},"PeriodicalIF":3.4,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262390","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