Signal Processing最新文献_第6页

A low computational complexity and high accuracy DOA estimation method in the hybrid analog-digital system with interleaved subarrays

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-02-04 DOI: 10.1016/j.sigpro.2025.109925

Guodong Wang, Yan Zhou

{"title":"A low computational complexity and high accuracy DOA estimation method in the hybrid analog-digital system with interleaved subarrays","authors":"Guodong Wang, Yan Zhou","doi":"10.1016/j.sigpro.2025.109925","DOIUrl":"10.1016/j.sigpro.2025.109925","url":null,"abstract":"<div><div>The large-scale, partially connected phase-shifter Hybrid Analog-Digital System (HADS) has attracted significant attention due to its low hardware complexity, high reconfigurability, and robustness to failures. Direction-of-Arrival (DOA) estimation presents a critical challenge in HADS, as it directly impacts the Signal-to-Noise Ratio (SNR) and throughput. Existing DOA estimation methods in HADS, however, are hindered by high complexity, the need for sign corrections, and ambiguity. This paper proposes an accurate and unambiguous DOA estimation method for HADS with interleaved subarrays (HADSIS) under specific phase shift conditions. The method utilizes cross-correlation between signals received by adjacent subarrays to directly estimate the DOA through coherent accumulation. This approach simplifies DOA estimation and eliminates ambiguity, thereby significantly enhancing estimation efficiency. Furthermore, the sign of the cross-correlation is uniquely determined by the parity of the number of subarrays, eliminating the need for further sign corrections during signal accumulation. Finally, simulation experiments are conducted to validate the performance of the proposed method. Our approach enhances the SNR of the cross-correlation results, thereby ensuring more accurate DOA estimation. Its key features include low computational complexity and high accuracy.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"232 ","pages":"Article 109925"},"PeriodicalIF":3.4,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349667","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

Alternating minimization algorithm for unlabeled sensing and linked linear regression

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-02-04 DOI: 10.1016/j.sigpro.2025.109927

Ahmed Ali Abbasi , Shuchin Aeron , Abiy Tasissa

引用次数: 0

Single-tone frequency estimation using modified autocorrelation and polynomial root-finding

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-02-03 DOI: 10.1016/j.sigpro.2025.109923

Hong-Cheng Liang, Hing Cheung So

引用次数: 0

Accelerated and enhanced multiplicative deblurring schemes

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-02-03 DOI: 10.1016/j.sigpro.2025.109910

Pierre-Alain Fayolle , Alexander G. Belyaev

引用次数: 0

Stochastic approximate inference of latent information in epidemic model: A data-driven approach

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-02-01 DOI: 10.1016/j.sigpro.2025.109919

Jungmin Kwon , Sujin Ahn , Hyunggon Park , Minhae Kwon

{"title":"Stochastic approximate inference of latent information in epidemic model: A data-driven approach","authors":"Jungmin Kwon , Sujin Ahn , Hyunggon Park , Minhae Kwon","doi":"10.1016/j.sigpro.2025.109919","DOIUrl":"10.1016/j.sigpro.2025.109919","url":null,"abstract":"<div><div>Precise estimates of disease transmissibility, made using mathematical methods, are a critical part of epidemiology. This paper proposes a stochastic compartmental model based on a Discrete-Time Markov chain (DTMC) to estimate the transmission rate and important latent variables, such as the number of hidden patients. To find the transmission rate that best represents the reported data (e.g., the number of confirmed cases), we formulate a maximum log-likelihood estimation problem. However, this problem is challenging because it includes the posterior distribution of the reported data, which is mathematically intractable. Therefore, we relax the problem by proposing surrogate optimization with stochastic approximation, which allows us to successfully estimate the transmission rate and latent variables. To assess the proposed inference model, extensive simulations are performed using datasets from COVID-19, seasonal influenza, and mpox. The results confirm that the proposed algorithm finds a transmission rate explaining the macroscopic and microscopic variations in the waves of infectious diseases. Compared with existing solutions, the proposed algorithm better explains real-world disease evolution, such as reinfection and microscopic fluctuations in waves.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"232 ","pages":"Article 109919"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378895","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 unified algorithmic framework for dynamic compressive sensing

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-02-01 DOI: 10.1016/j.sigpro.2025.109926

Xiaozhi Liu, Yong Xia

{"title":"A unified algorithmic framework for dynamic compressive sensing","authors":"Xiaozhi Liu, Yong Xia","doi":"10.1016/j.sigpro.2025.109926","DOIUrl":"10.1016/j.sigpro.2025.109926","url":null,"abstract":"<div><div>We present a unified algorithmic framework, termed PLAY-CS, for dynamic tracking and reconstruction of signal sequences exhibiting intrinsic structured dynamic sparsity. By leveraging specific statistical assumptions on the dynamic filtering of these sequences, our framework integrates a variety of existing dynamic compressive sensing (DCS) algorithms. This is facilitated by the introduction of a novel Partial-Laplacian filtering sparsity model, which is designed to capture more complex dynamic sparsity patterns. Within this unified DCS framework, we derive a new algorithm, <span><math><msup><mrow><mtext>PLAY</mtext></mrow><mrow><mo>+</mo></mrow></msup></math></span>-CS. Notably, the <span><math><msup><mrow><mtext>PLAY</mtext></mrow><mrow><mo>+</mo></mrow></msup></math></span>-CS algorithm eliminates the need for a priori knowledge of dynamic signal parameters, as these are adaptively learned through an expectation–maximization framework. Moreover, we extend the <span><math><msup><mrow><mtext>PLAY</mtext></mrow><mrow><mo>+</mo></mrow></msup></math></span>-CS algorithm to tackle the dynamic joint sparse signal reconstruction problem involving multiple measurement vectors. The proposed framework demonstrates superior performance in practical applications, such as real-time massive multiple-input multiple-output (MIMO) communication for dynamic channel tracking and background subtraction from online compressive measurements, outperforming existing DCS algorithms.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"232 ","pages":"Article 109926"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168356","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

DC-Reg: A triple-task collaborative framework for few-shot biomedical image registration

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-31 DOI: 10.1016/j.sigpro.2025.109924

Jun Wu , Yong Zhang , Zaiyang Tao , Meng Li , Tingting Han , Yuanyuan Li , Lingfei Zhu , Yiwei Niu , Lei Qu

{"title":"DC-Reg: A triple-task collaborative framework for few-shot biomedical image registration","authors":"Jun Wu , Yong Zhang , Zaiyang Tao , Meng Li , Tingting Han , Yuanyuan Li , Lingfei Zhu , Yiwei Niu , Lei Qu","doi":"10.1016/j.sigpro.2025.109924","DOIUrl":"10.1016/j.sigpro.2025.109924","url":null,"abstract":"<div><div>Deep learning (DL)-based deformable biomedical image registration (DIR) enables automatic information fusion and facilitates rapid diagnosis by aligning multi-source data into a unified coordinate system. However, achieving accurate similarity measurement and obtaining adequate training data pose significant challenges in biomedical image processing tasks. In this paper, we propose a few-shot DIR method that leverages spatial encoding within a triple-task collaborative framework to solve these issues. Firstly, we propose a registration network based on the spatial encoding, which represent images using voxel spatial positions, highlighting the importance of structural information in registration network while reducing modality difference between different images. Secondly, we propose a segmentation network based on data augmentation, which is achieved through the registration network. Specifically, we have designed a contrastive learning based discrimination network to suppress the unreliable augmented training data, which is also our third important component of the collaborative framework. Furthermore, the discrimination network also automatically learns similarity measure for the registration network. By iteratively refining the segmentation, registration, and discrimination networks, we are able to obtain a highly accurate registration model. Our experimental results on four mono-modal and multi-modal datasets demonstrate the effectiveness and superiority of the proposed method.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"232 ","pages":"Article 109924"},"PeriodicalIF":3.4,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168358","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

Embedding a heavy-ball type of momentum into the estimating sequences

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-31 DOI: 10.1016/j.sigpro.2024.109865

Endrit Dosti , Sergiy A. Vorobyov , Themistoklis Charalambous

{"title":"Embedding a heavy-ball type of momentum into the estimating sequences","authors":"Endrit Dosti , Sergiy A. Vorobyov , Themistoklis Charalambous","doi":"10.1016/j.sigpro.2024.109865","DOIUrl":"10.1016/j.sigpro.2024.109865","url":null,"abstract":"<div><div>We present a new accelerated gradient-based method for solving smooth unconstrained optimization problems. The new method exploits additional information about the objective function and is built by embedding a heavy-ball type of momentum into the Fast Gradient Method (FGM). We devise a generalization of the estimating sequences, which allows for encoding any form of information about the objective function that can aid in further accelerating the minimization process. In the black box framework, we propose a construction for the generalized estimating sequences, which is obtained by exploiting the history of the previously constructed estimating functions. Moreover, we prove that the proposed method requires at most <span><math><mrow><msqrt><mrow><mfrac><mrow><mi>κ</mi></mrow><mrow><mn>2</mn></mrow></mfrac></mrow></msqrt><mfenced><mrow><mo>ln</mo><mfrac><mrow><mn>1</mn></mrow><mrow><mi>ϵ</mi></mrow></mfrac><mo>+</mo><mi>O</mi><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow></mrow></mfenced></mrow></math></span> iterations to find a point <span><math><mi>x</mi></math></span> with <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow><mo>−</mo><msup><mrow><mi>f</mi></mrow><mrow><mo>∗</mo></mrow></msup><mo>≤</mo><mi>ϵ</mi></mrow></math></span>, where <span><math><mi>ϵ</mi></math></span> is the desired tolerance and <span><math><mi>κ</mi></math></span> is the condition number of the problem. Our theoretical results are corroborated by numerical experiments on various types of optimization problems, often dealt with in different areas of the information processing sciences. Both synthetic and real-world datasets are utilized to demonstrate the efficiency of our proposed method in terms of decreasing the distance to the optimal solution, the norm of the gradient and the function value.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"233 ","pages":"Article 109865"},"PeriodicalIF":3.4,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403336","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}

引用次数: 0

Finite blocklength approach for the two-user MISO multiple-access channel with noisy feedback and its performance analysis

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-31 DOI: 10.1016/j.sigpro.2025.109900

Guangfen Xie , Rong Luo , Bin Dai

引用次数: 0

Performance analysis of trace proximity based distributed Kalman filter

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-01-30 DOI: 10.1016/j.sigpro.2025.109906

Wei Liu , Peng Shi , Shuoyu Wang

{"title":"Performance analysis of trace proximity based distributed Kalman filter","authors":"Wei Liu , Peng Shi , Shuoyu Wang","doi":"10.1016/j.sigpro.2025.109906","DOIUrl":"10.1016/j.sigpro.2025.109906","url":null,"abstract":"<div><div>This paper analyzes the performance of the distributed Kalman filter based on trace proximity criterion and neighboring-node measurements (TPCNM) proposed in Liu et al. (2022) where the performance analysis includes the boundness, convergence, mean square error and estimation error covariance. First, we prove that the boundness of the distributed Kalman filter based on TPCNM is ensured under proper conditions. Second, the convergence conditions for the distributed Kalman filter based on TPCNM with some constraints for the value of node are established using a novel matrix difference equation (MDE), two equalities in the distributed Kalman filter based on TPCNM and some results presented in this paper where one equality contains a term with both the measurement matrix and the measurement noise covariance matrix. In addition, the mean square error performance for the distributed Kalman filter based on TPCNM is analyzed, and it is proved that the matrix <span><math><mi>P</mi></math></span> in the distributed Kalman filter based on TPCNM is the real estimation error covariance. A scalar dynamic system example and a radar tracking example are provided to illustrate the validity and correctness of the developed methods.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"232 ","pages":"Article 109906"},"PeriodicalIF":3.4,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168360","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