Signal Processing最新文献_第10页

Elliptical Wishart distributions: Information geometry, maximum likelihood estimator, performance analysis and statistical learning 椭圆Wishart分布：信息几何，最大似然估计，性能分析和统计学习

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-03-08 DOI: 10.1016/j.sigpro.2025.109981

Imen Ayadi, Florent Bouchard, Frédéric Pascal

引用次数: 0

Phase differences estimation for diagonal ULAs within a fully filled rectangular array based on degenerated spatial ARMA process 基于退化空间ARMA过程的全填充矩形阵列对角线ula相位差估计

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-03-08 DOI: 10.1016/j.sigpro.2025.109985

Guijin Yao, Jiye Liu, Hairong Zhang, Yue Li

{"title":"Phase differences estimation for diagonal ULAs within a fully filled rectangular array based on degenerated spatial ARMA process","authors":"Guijin Yao, Jiye Liu, Hairong Zhang, Yue Li","doi":"10.1016/j.sigpro.2025.109985","DOIUrl":"10.1016/j.sigpro.2025.109985","url":null,"abstract":"<div><div>A generalized estimation method of diagonal phase differences of external sources incident upon a fully-filled rectangular array (FFRA) is proposed based on degenerate spatial <em>ARMA</em> process. Various diagonal uniform linear arrays (ULAs) within FFRAs are first classified by function forms of diagonal phase differences, the ULAs sharing the same diagonal phase difference belong to one category. The modified Yule–Walker (MYW) system of linear equations and the root-finding polynomial are first derived for FFRA ULAs. Owing to diagonal interspacings larger than half of carrier wavelength, ambiguity problem of diagonal phase differences has arisen in estimation. Utilizing the explicit linear-combination relationships satisfied by diagonal and axial phase differences, a simple and effective elimination scheme of estimate ambiguity of diagonal phase differences is proposed in which actual intervals of no ambiguity are deduced by making use of the estimates of axial phase differences. With different FFRA diagonal ULAs on <span><math><mrow><mi>X</mi><mo>−</mo><mi>Y</mi></mrow></math></span> sensor plane, it is numerically manifested by Monte-Carlo trials that the proposed method is effective for both independent and coherent sources and the Root mean square errors (RMSEs) are slowly convergent to the corresponding Cramer–Rao bounds (CRBs) after estimate ambiguities are eliminated. The consistency of estimation performance for diagonal ULAs belonging to one category is exhibited by their identical RMSEs. Because of the ability to exploit axial and diagonal ULAs, the proposed estimation method provides the basis of two-dimensional direction of arrival (2-D DoA) estimation with ULA combinations of FFRAs.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"234 ","pages":"Article 109985"},"PeriodicalIF":3.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601400","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

EscapeTrack: Multi-object tracking with estimated camera parameters EscapeTrack：多目标跟踪与估计的相机参数

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-03-06 DOI: 10.1016/j.sigpro.2025.109958

Kefu Yi , Hao Wu , Wei Hao , Rongdong Hu

{"title":"EscapeTrack: Multi-object tracking with estimated camera parameters","authors":"Kefu Yi , Hao Wu , Wei Hao , Rongdong Hu","doi":"10.1016/j.sigpro.2025.109958","DOIUrl":"10.1016/j.sigpro.2025.109958","url":null,"abstract":"<div><div>Multi-object tracking (MOT) remains a challenging task in dynamic environments. While most 2D tracking methods focus solely on the image plane, they often neglect the Ground Plane Assumption (GPA) — the principle that targets typically move on a consistent ground plane. This is because camera parameters are difficult to obtain and are not very reliable in scenarios involving camera motion or where the GPA does not apply. To address this issue, we propose EscapeTrack, a novel MOT algorithm that robustly handles imprecise camera parameters. Unlike conventional homography projection methods prone to calibration errors, EscapeTrack innovatively models target coordinates on the ground plane as latent variables within a Kalman filter framework. By constructing an observation model that projects these latent states onto the image plane, our method achieves superior tracking accuracy even with significant parameter noise. Extensive evaluations demonstrate state-of-the-art performance on MOT17, MOT20, DanceTrack, SportsMOT, and BDD100K benchmarks. Notably, EscapeTrack excels in scenarios with camera motion or GPA violations, by inherently treating such cases as camera parameter estimation errors. This robustness enables practical deployment in real-world systems where precise calibration is infeasible, advancing intelligent tracking in complex dynamic environments. The source code will be available at <span><span>https://github.com/corfyi/EscapeTrack</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"234 ","pages":"Article 109958"},"PeriodicalIF":3.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601401","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 reduced-order Kalman consensus filter for multisensor networked descriptor systems 多传感器网络广义系统的分布式降阶卡尔曼一致滤波

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-03-05 DOI: 10.1016/j.sigpro.2025.109991

Minghu Zhang, Shuli Sun

{"title":"Distributed reduced-order Kalman consensus filter for multisensor networked descriptor systems","authors":"Minghu Zhang, Shuli Sun","doi":"10.1016/j.sigpro.2025.109991","DOIUrl":"10.1016/j.sigpro.2025.109991","url":null,"abstract":"<div><div>In the context of multisensor linear discrete networked descriptor systems, an equivalence transformation, achieved via singular value decomposition, leads to the derivation of two lower-dimensional non-descriptor subsystems. Each network node can perform state estimation based on data of its own and its neighboring nodes. Applying the Kalman consensus filter (KCF) framework, wherein one-step prediction estimates of a reduced-order subsystem are exchanged among network nodes, a distributed reduced-order KCF is designed for each sensor node, incorporating multiple consensus gains. This design facilitates collaborative state estimation by enabling nodes to leverage both their own measurements and the prediction estimates received from their neighbors. The optimal Kalman filtering gains and the optimal consensus filtering gains are determined by minimizing the trace of the filtering error covariance matrix. The investigation delves into the stability and steady-state characteristics of the tailored distributed reducer-order filtering systems. The performance of the algorithms is confirmed through illustrative simulation cases.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"234 ","pages":"Article 109991"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591983","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

Data-reuse recursive least-squares algorithm with Riemannian manifold constraint 黎曼流形约束下的数据重用递归最小二乘算法

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-03-04 DOI: 10.1016/j.sigpro.2025.109982

Haiquan Zhao, Haolin Wang, Yi Peng

引用次数: 0

Triple-layer representation of low rank and group sparsity for hyperspectral image denoising 高光谱图像去噪的低秩和群稀疏度三层表示

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-03-04 DOI: 10.1016/j.sigpro.2025.109960

Yangyang Song, Xiaozhen Xie

{"title":"Triple-layer representation of low rank and group sparsity for hyperspectral image denoising","authors":"Yangyang Song, Xiaozhen Xie","doi":"10.1016/j.sigpro.2025.109960","DOIUrl":"10.1016/j.sigpro.2025.109960","url":null,"abstract":"<div><div>Hyperspectral image (HSI) denoising is an essential step in image processing. In the regularization-based approaches for this step, various kinds of prior information are investigated only in the original or one-layer transform domains of HSIs. To sufficiently explore deeper priors, we propose a novel triple-layer representation of low-rankness and group sparsity (TLLRGS) for HSI denoising. This method encodes the prior knowledge of HSIs with two low-rank layers and a single group-sparse layer. Specifically, the globally low rank in the original domain is measured by Tucker decomposition in the first layer. Then, the low rank in the gradient domain is captured via orthogonal transforms, which can be regarded as the second layer of our TLLRGS model. To describe the shared sparse pattern in the subspaces of gradient domains, we design an <span><math><msub><mrow><mi>l</mi></mrow><mrow><mn>2</mn><mo>,</mo><mi>γ</mi></mrow></msub></math></span>-norm with the parameter <span><math><mi>γ</mi></math></span> in the third layer. Additionally, we introduce <span><math><msub><mrow><mi>l</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-norm regularization for complex noise, especially sparse noise. To solve the TLLRGS model, we adopt an iterative approach based on the augmented Lagrange multiplier method. Finally, extensive experimental results involving complex noise removal demonstrate the superiority of the TLLRGS model over several state-of-the-art denoising methods.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"233 ","pages":"Article 109960"},"PeriodicalIF":3.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548882","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

Outlier-robust tri-percentile and truncated maximum likelihood estimators of parameters of weibull radar clutter 威布尔雷达杂波参数的离群鲁棒三百分位和截断极大似然估计

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-03-02 DOI: 10.1016/j.sigpro.2025.109986

Peng-Jia Zou, Peng-Lang Shui, Xiang Liang

{"title":"Outlier-robust tri-percentile and truncated maximum likelihood estimators of parameters of weibull radar clutter","authors":"Peng-Jia Zou, Peng-Lang Shui, Xiang Liang","doi":"10.1016/j.sigpro.2025.109986","DOIUrl":"10.1016/j.sigpro.2025.109986","url":null,"abstract":"<div><div>Weibull distributions have gained much concern for the versatility in modelling radar clutter such as sea, ground, and weather clutters. Most existing parameter estimation methods are sensitive to outliers and have degraded accuracy in real clutter environments with outliers. This paper proposes two classes of outlier-robust parameter estimators of Weibull distribution. One is the tri-percentile (TriP) estimator, where the shape parameter is estimated from the ratio of two sample percentiles and the scale parameter is estimated from the third sample percentile. The relative root mean square error (RRMSE) of the shape parameter is proved to be independent of the two parameters. Moreover, the optimal position setup of the percentiles is chosen to minimize estimation errors. The other is the iterative truncated maximum likelihood (TML) estimator, which obtains more accurate robust estimates. It is shown that the RRMSE of the shape parameter is also independent of the two parameters. The ML estimator is a special example of the iterative TML estimator. Finally, experiments with simulated data and measured radar data are made to compare the performance of the TriP and TML estimators with that of the ML estimators and other existing estimators in the presence of outliers in data.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"234 ","pages":"Article 109986"},"PeriodicalIF":3.4,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551372","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

Distance of mean embedding for testing independence of functional data 均值嵌入距离用于测试功能数据的独立性

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-03-01 DOI: 10.1016/j.sigpro.2025.109959

Mirosław Krzyśko , Łukasz Smaga , Jędrzej Wydra

引用次数: 0

Deep evidential reasoning rule learning 深度证据推理规则学习

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-02-27 DOI: 10.1016/j.sigpro.2025.109984

Hui Liu , Zhiguo Zhou

{"title":"Deep evidential reasoning rule learning","authors":"Hui Liu , Zhiguo Zhou","doi":"10.1016/j.sigpro.2025.109984","DOIUrl":"10.1016/j.sigpro.2025.109984","url":null,"abstract":"<div><div>Deep learning has achieved great success in the past years. However, due to the uncertainty in the real world, the concerns on building reliable models have been raised. However, most current strategies can't achieve this goal in a unified way. Since the recently developed evidential reasoning rule (ER<sup>2</sup>) which is a general and interpretable probabilistic inference engine can integrate reliability to realize adaptive evidence combination and overall reliability is introduced to measure the credibility of output, it is an ideal strategy to help deep learning build more reliable model. As such, a new deep evidential reasoning rule learning method (DER<sup>2</sup>) is developed in this study. DER<sup>2</sup> consists of training, adaptation and testing stage. In training stage, deep neural network with multiple fully connected layers is trained. In adaptation stage, reliability is introduced to tune the trained model to obtain the adapted output for a given test sample. In testing stage, not only the predictive output probability is obtained, but also the overall reliability is estimated to measure the credibility of model output so that the decision maker can determine whether the predictive results should be trusted or not. Meanwhile, the model output can be interpreted through the case-based way. The experimental results demonstrated that DER<sup>2</sup> can obtain better performance when introducing adaptation stage and a high-quality credibility measurement can be realized through overall reliability as well.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"233 ","pages":"Article 109984"},"PeriodicalIF":3.4,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549000","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

Split-complex feedforward neural network for GFDM joint channel equalization and signal detection 分割-复杂前馈神经网络用于GFDM联合信道均衡和信号检测

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-02-27 DOI: 10.1016/j.sigpro.2025.109956

Marcos Nascimento , Candice Müller , Kayol S. Mayer

{"title":"Split-complex feedforward neural network for GFDM joint channel equalization and signal detection","authors":"Marcos Nascimento , Candice Müller , Kayol S. Mayer","doi":"10.1016/j.sigpro.2025.109956","DOIUrl":"10.1016/j.sigpro.2025.109956","url":null,"abstract":"<div><div>This paper presents a novel approach for channel equalization and signal detection in generalized frequency division multiplexing (GFDM) systems, designed for dispersive channels and capable of handling nonlinearities. In digital communications systems, deep learning (DL) techniques have emerged as a promising alternative to traditional adaptive digital signal processing. Although DL is a trending topic and has been applied to areas such as beamforming, channel estimation, equalization, and decoding, there is limited research on the use of complex-valued neural networks (CVNN), particularly in the context of GFDM systems. In this work, we propose a joint channel equalization and signal detection approach for GFDM based on the fully connected CVNN split-complex feedforward neural network (SCFNN). The proposed SCFNN effectively equalizes the dispersive 5G channel while concurrently detects the symbols non-orthogonally multiplexed in frequency, handling both scenarios with and without clipping, all within a single SCFNN. Results are compared with classical equalization and detection algorithms, as well as with the fully connected real-valued neural network (RVNN) approach. The proposed SCFNN solution presents superior symbol error rate (SER) performance while maintaining computational complexity on par with conventional methods.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"233 ","pages":"Article 109956"},"PeriodicalIF":3.4,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549001","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