Pub Date : 2025-01-17DOI: 10.1109/LSP.2024.3505798
Shasha Mao;Yuanyuan Zhang;Dandan Yan;Puhua Chen
Due to labeling subjectivity, label noises have become a critical issue that is addressed in facial expression recognition. From the view of human visual perception, the facial exhibited emotion characteristic should be unaltered corresponding to its truth expression, rather than the noise label, whereas most methods ignore the emotion consistency during FER, especially from different networks. Based on this, we propose a new FER method based heterogeneous dual-branch emotional consistency constrains, to prevent the model from memorizing noise samples based on features associated with noisy labels. In the proposed method, the emotion consistency from spatial transformation and heterogeneous networks are simultaneously considered to guide the model to perceive the overall visual features of expressions. Meanwhile, the confidence of the given label is evaluated based on emotional attention maps of original and transformed images, which effectively enhances the classification reliability of two branches to alleviate the negative effect of noisy labels in the learning process. Additionally, the weighted ensemble strategy is used to unify two branches. Experimental results illustrate that the proposed method achieves better performance than the state-of-the-art methods for 10%, 20% and 30% label noises.
{"title":"Heterogeneous Dual-Branch Emotional Consistency Network for Facial Expression Recognition","authors":"Shasha Mao;Yuanyuan Zhang;Dandan Yan;Puhua Chen","doi":"10.1109/LSP.2024.3505798","DOIUrl":"https://doi.org/10.1109/LSP.2024.3505798","url":null,"abstract":"Due to labeling subjectivity, label noises have become a critical issue that is addressed in facial expression recognition. From the view of human visual perception, the facial exhibited emotion characteristic should be unaltered corresponding to its truth expression, rather than the noise label, whereas most methods ignore the emotion consistency during FER, especially from different networks. Based on this, we propose a new FER method based heterogeneous dual-branch emotional consistency constrains, to prevent the model from memorizing noise samples based on features associated with noisy labels. In the proposed method, the emotion consistency from spatial transformation and heterogeneous networks are simultaneously considered to guide the model to perceive the overall visual features of expressions. Meanwhile, the confidence of the given label is evaluated based on emotional attention maps of original and transformed images, which effectively enhances the classification reliability of two branches to alleviate the negative effect of noisy labels in the learning process. Additionally, the weighted ensemble strategy is used to unify two branches. Experimental results illustrate that the proposed method achieves better performance than the state-of-the-art methods for 10%, 20% and 30% label noises.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"566-570"},"PeriodicalIF":3.2,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993083","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}
Pub Date : 2025-01-08DOI: 10.1109/LSP.2025.3527196
Zijie Lou;Gang Cao;Man Lin
Video inpainting techniques typically serve to restore destroyed or missing regions in digital videos. However, such techniques may also be illegally used to remove important objects for creating forged videos. This letter proposes a simple yet effective forensic scheme for Video Inpainting LOcalization with ContrAstive Learning (ViLocal). A 3D Uniformer encoder is applied to the video noise residual for learning effective spatiotemporal features. To enhance discriminative power, supervised contrastive learning is adopted to capture the local regional inconsistency through separating the pristine and inpainted pixels. The pixel-wise inpainting localization map is yielded by a lightweight convolution decoder with two-stage training. To prepare enough training samples, we build a video object segmentation dataset (VOS2k5) of 2500 videos with pixel-level annotations per frame. Extensive experimental results validate the superiority of ViLocal over the state-of-the-arts.
{"title":"Video Inpainting Localization With Contrastive Learning","authors":"Zijie Lou;Gang Cao;Man Lin","doi":"10.1109/LSP.2025.3527196","DOIUrl":"https://doi.org/10.1109/LSP.2025.3527196","url":null,"abstract":"Video inpainting techniques typically serve to restore destroyed or missing regions in digital videos. However, such techniques may also be illegally used to remove important objects for creating forged videos. This letter proposes a simple yet effective forensic scheme for Video Inpainting LOcalization with ContrAstive Learning (ViLocal). A 3D Uniformer encoder is applied to the video noise residual for learning effective spatiotemporal features. To enhance discriminative power, supervised contrastive learning is adopted to capture the local regional inconsistency through separating the pristine and inpainted pixels. The pixel-wise inpainting localization map is yielded by a lightweight convolution decoder with two-stage training. To prepare enough training samples, we build a video object segmentation dataset (VOS2k5) of 2500 videos with pixel-level annotations per frame. Extensive experimental results validate the superiority of ViLocal over the state-of-the-arts.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"611-615"},"PeriodicalIF":3.2,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993346","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}
Pub Date : 2025-01-08DOI: 10.1109/LSP.2025.3527197
Hao Zhang;Ping Lu;Te Qi;Yan Xu;Tieyong Zeng
Image dehazing is regarded as a fundamental image processing task with a major impact on higher-level imaging tasks. Many existing haze removal methods are designed for homogeneous haze, but in real-world cases, the haze is normally non-homogeneous. Superpixels, which segment an image into a set of closely spaced regions, can be employed in real-world scenarios to deal with non-homogeneous haze. In our paper, an adaptive non-homogeneous image dehazing approach that utilizes the superpixel-guided algorithm is designed to segment different hazy regions. Considering that both ambient light and transmission map estimation have a significant impact on the results, our research focuses on the development of a variational dehazing model that takes into account non-uniform ambient light and non-uniform transmission maps to address varying levels of haze. A series of numerical results illustrate the superiority and efficacy of our method.
{"title":"Adaptive Superpixel-Guided Non-Homogeneous Image Dehazing","authors":"Hao Zhang;Ping Lu;Te Qi;Yan Xu;Tieyong Zeng","doi":"10.1109/LSP.2025.3527197","DOIUrl":"https://doi.org/10.1109/LSP.2025.3527197","url":null,"abstract":"Image dehazing is regarded as a fundamental image processing task with a major impact on higher-level imaging tasks. Many existing haze removal methods are designed for homogeneous haze, but in real-world cases, the haze is normally non-homogeneous. Superpixels, which segment an image into a set of closely spaced regions, can be employed in real-world scenarios to deal with non-homogeneous haze. In our paper, an adaptive non-homogeneous image dehazing approach that utilizes the superpixel-guided algorithm is designed to segment different hazy regions. Considering that both ambient light and transmission map estimation have a significant impact on the results, our research focuses on the development of a variational dehazing model that takes into account non-uniform ambient light and non-uniform transmission maps to address varying levels of haze. A series of numerical results illustrate the superiority and efficacy of our method.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"591-595"},"PeriodicalIF":3.2,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10833755","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multi-view clustering has attracted significant attention in recent years because it can leverage the consistent and complementary information of multiple views to improve clustering performance. However, effectively fuse the information and balance the consistent and complementary information of multiple views are common challenges faced by multi-view clustering. Most existing multi-view fusion works focus on weighted-sum fusion and concatenating fusion, which unable to fully fuse the underlying information, and not consider balancing the consistent and complementary information of multiple views. To this end, we propose Cross-view Fusion for Multi-view Clustering (CFMVC). Specifically, CFMVC combines deep neural network and graph convolutional network for cross-view information fusion, which fully fuses feature information and structural information of multiple views. In order to balance the consistent and complementary information of multiple views, CFMVC enhances the correlation among the same samples to maximize the consistent information while simultaneously reinforcing the independence among different samples to maximize the complementary information. Experimental results on several multi-view datasets demonstrate the effectiveness of CFMVC for multi-view clustering task.
{"title":"Cross-View Fusion for Multi-View Clustering","authors":"Zhijie Huang;Binqiang Huang;Qinghai Zheng;Yuanlong Yu","doi":"10.1109/LSP.2025.3527231","DOIUrl":"https://doi.org/10.1109/LSP.2025.3527231","url":null,"abstract":"Multi-view clustering has attracted significant attention in recent years because it can leverage the consistent and complementary information of multiple views to improve clustering performance. However, effectively fuse the information and balance the consistent and complementary information of multiple views are common challenges faced by multi-view clustering. Most existing multi-view fusion works focus on weighted-sum fusion and concatenating fusion, which unable to fully fuse the underlying information, and not consider balancing the consistent and complementary information of multiple views. To this end, we propose Cross-view Fusion for Multi-view Clustering (CFMVC). Specifically, CFMVC combines deep neural network and graph convolutional network for cross-view information fusion, which fully fuses feature information and structural information of multiple views. In order to balance the consistent and complementary information of multiple views, CFMVC enhances the correlation among the same samples to maximize the consistent information while simultaneously reinforcing the independence among different samples to maximize the complementary information. Experimental results on several multi-view datasets demonstrate the effectiveness of CFMVC for multi-view clustering task.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"621-625"},"PeriodicalIF":3.2,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993445","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}
Pub Date : 2025-01-06DOI: 10.1109/LSP.2025.3525855
Yue Wu;Fangfang Qiang;Wujie Zhou;Weiqing Yan
Applying computer vision techniques to rail surface defect detection (RSDD) is crucial for preventing catastrophic accidents. However, challenges such as complex backgrounds and irregular defect shapes persist. Previous methods have focused on extracting salient object information from a pixel perspective, thereby neglecting valuable high- and low-frequency image information, which can better capture global structural information. In this study, we design a pixel-aware frequency conversion network (PFCNet) to explore RSDD from a frequency domain perspective. We use different attention mechanisms and frequency enhancement for high-level and shallow features to explore local details and global structures comprehensively. In addition, we design a dual-control reorganization module to refine the features across levels. We conducted extensive experiments on an industrial RGB-D dataset (NEU RSDDS-AUG), and PFCNet achieved superior performance.
{"title":"PFCNet: Enhancing Rail Surface Defect Detection With Pixel-Aware Frequency Conversion Networks","authors":"Yue Wu;Fangfang Qiang;Wujie Zhou;Weiqing Yan","doi":"10.1109/LSP.2025.3525855","DOIUrl":"https://doi.org/10.1109/LSP.2025.3525855","url":null,"abstract":"Applying computer vision techniques to rail surface defect detection (RSDD) is crucial for preventing catastrophic accidents. However, challenges such as complex backgrounds and irregular defect shapes persist. Previous methods have focused on extracting salient object information from a pixel perspective, thereby neglecting valuable high- and low-frequency image information, which can better capture global structural information. In this study, we design a pixel-aware frequency conversion network (PFCNet) to explore RSDD from a frequency domain perspective. We use different attention mechanisms and frequency enhancement for high-level and shallow features to explore local details and global structures comprehensively. In addition, we design a dual-control reorganization module to refine the features across levels. We conducted extensive experiments on an industrial RGB-D dataset (NEU RSDDS-AUG), and PFCNet achieved superior performance.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"606-610"},"PeriodicalIF":3.2,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993097","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}
Pub Date : 2025-01-06DOI: 10.1109/LSP.2024.3521326
Cheng Wang;Zhen Mei;Jun Li;Kui Cai;Lingjun Kong
Accurate modeling and estimation of the threshold voltages of the flash memory can facilitate the efficient design of channel codes and detectors. However, most flash memory channel models are based on Gaussian distributions, which fail to capture certain key properties of the threshold voltages, such as their heavy-tails. To enhance the model accuracy, we first propose a piecewise student's t-distribution mixture model (PSTMM), which features degrees of freedom to control the left and right tails of the voltage distributions. We further propose an PSTMM based expectation maximization (PSTMM-EM) algorithm to estimate model parameters for flash memories by alternately computing the expected values of the missing data and maximizing the likelihood function with respect to the model parameters. Simulation results demonstrate that our proposed algorithm exhibits superior stability and can effectively extend the flash memory lifespan by 1700 program/erase (PE) cycles compared with the existing parameter estimation algorithms.
{"title":"Piecewise Student's t-distribution Mixture Model-Based Estimation for NAND Flash Memory Channels","authors":"Cheng Wang;Zhen Mei;Jun Li;Kui Cai;Lingjun Kong","doi":"10.1109/LSP.2024.3521326","DOIUrl":"https://doi.org/10.1109/LSP.2024.3521326","url":null,"abstract":"Accurate modeling and estimation of the threshold voltages of the flash memory can facilitate the efficient design of channel codes and detectors. However, most flash memory channel models are based on Gaussian distributions, which fail to capture certain key properties of the threshold voltages, such as their heavy-tails. To enhance the model accuracy, we first propose a piecewise student's t-distribution mixture model (PSTMM), which features degrees of freedom to control the left and right tails of the voltage distributions. We further propose an PSTMM based expectation maximization (PSTMM-EM) algorithm to estimate model parameters for flash memories by alternately computing the expected values of the missing data and maximizing the likelihood function with respect to the model parameters. Simulation results demonstrate that our proposed algorithm exhibits superior stability and can effectively extend the flash memory lifespan by 1700 program/erase (PE) cycles compared with the existing parameter estimation algorithms.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"451-455"},"PeriodicalIF":3.2,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938333","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}
Pub Date : 2025-01-06DOI: 10.1109/LSP.2025.3526094
Jiayue Zhao;Shiman Li;Yi Hao;Chenxi Zhang
Color fundus photography (CFP) and optical coherence tomography (OCT) are two common modalities used in eye disease screening, providing crucial complementary information for the diagnosis of eye diseases. However, existing multimodal learning methods cannot fully leverage the information from each modality due to the large dimensional and semantic gap between 2D CFP and 3D OCT images, leading to suboptimal classification performance. To bridge the modality gap and fully exploit the information from each modality, we propose a novel feature disentanglement method that decomposes features into modality-shared and modality-specific components. We design a multi-level regularization strategy including intra-modality, inter-modality, and intra-inter-modality regularization to facilitate the effective learning of the modality Shared-Specific features. Our method achieves state-of-the-art performance on two eye disease diagnosis tasks using two publicly available datasets. Our method promises to serve as a useful tool for multimodal eye disease diagnosis.
{"title":"Bridging the Modality Gap in Multimodal Eye Disease Screening: Learning Modality Shared-Specific Features via Multi-Level Regularization","authors":"Jiayue Zhao;Shiman Li;Yi Hao;Chenxi Zhang","doi":"10.1109/LSP.2025.3526094","DOIUrl":"https://doi.org/10.1109/LSP.2025.3526094","url":null,"abstract":"Color fundus photography (CFP) and optical coherence tomography (OCT) are two common modalities used in eye disease screening, providing crucial complementary information for the diagnosis of eye diseases. However, existing multimodal learning methods cannot fully leverage the information from each modality due to the large dimensional and semantic gap between 2D CFP and 3D OCT images, leading to suboptimal classification performance. To bridge the modality gap and fully exploit the information from each modality, we propose a novel feature disentanglement method that decomposes features into modality-shared and modality-specific components. We design a multi-level regularization strategy including intra-modality, inter-modality, and intra-inter-modality regularization to facilitate the effective learning of the modality Shared-Specific features. Our method achieves state-of-the-art performance on two eye disease diagnosis tasks using two publicly available datasets. Our method promises to serve as a useful tool for multimodal eye disease diagnosis.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"586-590"},"PeriodicalIF":3.2,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993094","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}
Pub Date : 2025-01-06DOI: 10.1109/LSP.2025.3525898
Majdoddin Esfandiari;Sergiy A. Vorobyov
A noise covariance matrix estimation approach in unknown noise field for direction finding applicable for the practically important cases of nonuniform and block-diagonal sensor noise is proposed. It is based on an alternating procedure that can be adjusted for a specific noise type. Numerical simulations are conducted in order to establish the generality and superiority of the proposed approach over the existing state-of-the-art methods, especially in challenging scenarios.
{"title":"Noise Covariance Matrix Estimation in Block-Correlated Noise Field for Direction Finding","authors":"Majdoddin Esfandiari;Sergiy A. Vorobyov","doi":"10.1109/LSP.2025.3525898","DOIUrl":"https://doi.org/10.1109/LSP.2025.3525898","url":null,"abstract":"A noise covariance matrix estimation approach in unknown noise field for direction finding applicable for the practically important cases of nonuniform and block-diagonal sensor noise is proposed. It is based on an alternating procedure that can be adjusted for a specific noise type. Numerical simulations are conducted in order to establish the generality and superiority of the proposed approach over the existing state-of-the-art methods, especially in challenging scenarios.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"531-535"},"PeriodicalIF":3.2,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10824965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1109/LSP.2025.3526092
Ángel F. García-Fernández;Giorgio Battistelli
This paper presents the consensus iterated posterior linearisation filter (IPLF) for distributed state estimation. The consensus IPLF algorithm is based on a measurement model described by its conditional mean and covariance given the state, and performs iterated statistical linear regressions of the measurements with respect to the current approximation of the posterior to improve estimation performance. Three variants of the algorithm are presented based on the type of consensus that is used: consensus on information, consensus on measurements, and hybrid consensus on measurements and information. Simulation results show the benefits of the proposed algorithm in distributed state estimation.
{"title":"Consensus Iterated Posterior Linearization Filter for Distributed State Estimation","authors":"Ángel F. García-Fernández;Giorgio Battistelli","doi":"10.1109/LSP.2025.3526092","DOIUrl":"https://doi.org/10.1109/LSP.2025.3526092","url":null,"abstract":"This paper presents the consensus iterated posterior linearisation filter (IPLF) for distributed state estimation. The consensus IPLF algorithm is based on a measurement model described by its conditional mean and covariance given the state, and performs iterated statistical linear regressions of the measurements with respect to the current approximation of the posterior to improve estimation performance. Three variants of the algorithm are presented based on the type of consensus that is used: consensus on information, consensus on measurements, and hybrid consensus on measurements and information. Simulation results show the benefits of the proposed algorithm in distributed state estimation.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"561-565"},"PeriodicalIF":3.2,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993082","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}
Pub Date : 2025-01-03DOI: 10.1109/LSP.2024.3525400
Sina Shahsavari;Piya Pal
This paper derives new non-asymptotic characterization of the Cramér-Rao Bound (CRB) of any sparse array as a function of the angular separation between two far-field narrowband sources in certain regimes characterized by a low Signal-to-Noise Ratio (SNR). The primary contribution is the derivation of matching upper and lower bounds on the CRB in a certain measurement-dependent SNR (MD-SNR) regime, where one can zoom into progressively lower SNR as the number of sensors increases. This tight characterization helps to establish that sparse arrays such as nested and coprime arrays provably exhibit lower CRB compared to Uniform Linear Arrays (ULAs) in the specified SNR regime.
{"title":"Cramér-Rao Bounds and Resolution Benefits of Sparse Arrays in Measurement-Dependent SNR Regimes","authors":"Sina Shahsavari;Piya Pal","doi":"10.1109/LSP.2024.3525400","DOIUrl":"https://doi.org/10.1109/LSP.2024.3525400","url":null,"abstract":"This paper derives new non-asymptotic characterization of the Cramér-Rao Bound (CRB) of any sparse array as a function of the angular separation between two far-field narrowband sources in certain regimes characterized by a low Signal-to-Noise Ratio (SNR). The primary contribution is the derivation of matching upper and lower bounds on the CRB in a certain measurement-dependent SNR (MD-SNR) regime, where one can zoom into progressively lower SNR as the number of sensors increases. This tight characterization helps to establish that sparse arrays such as nested and coprime arrays provably exhibit lower CRB compared to Uniform Linear Arrays (ULAs) in the specified SNR regime.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"601-605"},"PeriodicalIF":3.2,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993096","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}
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