IEEE Transactions on Signal Processing最新文献_第5页

Tensor Completion Network for Visual Data 用于可视化数据的张量补全网络

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-01-01 DOI: 10.1109/TSP.2024.3524568

Xiang-Yu Wang;Xiao-Peng Li;Nicholas D. Sidiropoulos;Hing Cheung So

{"title":"Tensor Completion Network for Visual Data","authors":"Xiang-Yu Wang;Xiao-Peng Li;Nicholas D. Sidiropoulos;Hing Cheung So","doi":"10.1109/TSP.2024.3524568","DOIUrl":"10.1109/TSP.2024.3524568","url":null,"abstract":"Tensor completion aims at filling in the missing elements of an incomplete tensor based on its partial observations, which is a popular approach for image inpainting. Most existing methods for visual data recovery can be categorized into traditional optimization-based and neural network-based methods. The former usually adopt a low-rank assumption to handle this ill-posed problem, enjoying good interpretability and generalization. However, as visual data are only approximately low rank, handcrafted low-rank priors may not capture the complex details properly, limiting the recovery performance. For neural network-based methods, despite their impressive performance in image inpainting, sufficient training data are required for parameter learning, and their generalization ability on the unseen data is a concern. In this paper, combining the advantages of these two distinct approaches, we propose a tensor <bold>C</b>ompletion neural <bold>Net</b>work (CNet) for visual data completion. The CNet is comprised of two parts, namely, the encoder and decoder. The encoder is designed by exploiting the CANDECOMP/PARAFAC decomposition to produce a low-rank embedding of the target tensor, whose mechanism is interpretable. To compensate the drawback of the low-rank constraint, a decoder consisting of several convolutional layers is introduced to refine the low-rank embedding. The CNet only uses the observations of the incomplete tensor to recover its missing entries and thus is free from large training datasets. Extensive experiments in inpainting color images, grayscale video sequences, hyperspectral images, color video sequences, and light field images are conducted to showcase the superiority of CNet over state-of-the-art methods in terms of restoration performance.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"386-400"},"PeriodicalIF":4.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911637","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

Identification of ARMAX Models With Noisy Input: A Parametric Frequency Domain Solution 带噪声输入的ARMAX模型辨识：一种参数频域解

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-23 DOI: 10.1109/TSP.2024.3522300

Shenglin Song;Erliang Zhang

引用次数: 0

Target Localization and Sensor Self-Calibration of Position and Synchronization by Range and Angle Measurements 基于距离和角度测量的目标定位和传感器位置与同步自校准

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-23 DOI: 10.1109/TSP.2024.3520909

Tianyi Jia;Xiaochuan Ke;Hongwei Liu;K. C. Ho;Hongtao Su

{"title":"Target Localization and Sensor Self-Calibration of Position and Synchronization by Range and Angle Measurements","authors":"Tianyi Jia;Xiaochuan Ke;Hongwei Liu;K. C. Ho;Hongtao Su","doi":"10.1109/TSP.2024.3520909","DOIUrl":"10.1109/TSP.2024.3520909","url":null,"abstract":"The sensor position uncertainties and synchronization offsets can cause substantial performance degradation if the sensors are not properly calibrated. This paper investigates the localization of a constant velocity moving target and the self-calibration of sensors using a sequence of range and azimuth measurements observed at successive instants. A theoretical study by the Cramer-Rao Lower Bound (CRLB) reveals that the sensor positions can only be self-calibrated when there are at least two sensors and synchronization offsets can be handled by joint estimation. A low complexity sequential closed-form solution is proposed to estimate the target position and velocity first, and the coordinates of each sensor and synchronization offset afterward. While less intuitive, the analysis shows that the closed-form solutions for both the target and sensor parameters can reach the CRLB accuracy under small Gaussian noise. We also develop a semidefinite programming (SDP) solution by semidefinite relaxation (SDR) for joint localization and calibration from the Maximum Likelihood formulation, which exhibits higher noise tolerance than the closed-form solution. Simulations validate the analysis and the performance of the proposed methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"340-355"},"PeriodicalIF":4.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879693","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

Normalizing Flow-Based Differentiable Particle Filters 归一化基于流的可微粒子滤波器

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-23 DOI: 10.1109/TSP.2024.3521338

Xiongjie Chen;Yunpeng Li

{"title":"Normalizing Flow-Based Differentiable Particle Filters","authors":"Xiongjie Chen;Yunpeng Li","doi":"10.1109/TSP.2024.3521338","DOIUrl":"10.1109/TSP.2024.3521338","url":null,"abstract":"Recently, there has been a surge of interest in incorporating neural networks into particle filters, e.g. differentiable particle filters, to perform joint sequential state estimation and model learning for nonlinear non-Gaussian state-space models in complex environments. Existing differentiable particle filters are mostly constructed with vanilla neural networks that do not allow density estimation. As a result, they are either restricted to a bootstrap particle filtering framework or employ predefined distribution families (e.g. Gaussian distributions), limiting their performance in more complex real-world scenarios. In this paper we present a differentiable particle filtering framework that uses (conditional) normalizing flows to build its dynamic model, proposal distribution, and measurement model. This not only enables valid probability densities but also allows the proposed method to adaptively learn these modules in a flexible way, without being restricted to predefined distribution families. We derive the theoretical properties of the proposed filters and evaluate the proposed normalizing flow-based differentiable particle filters’ performance through a series of numerical experiments.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"493-507"},"PeriodicalIF":4.6,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879652","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

Three-Dimensional Localization of Mixed Near-Field and Far-Field Sources Based on a Unified Exact Propagation Model 基于统一精确传播模型的近场和远场混合源三维定位

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-20 DOI: 10.1109/TSP.2024.3520551

Jiaxiong Fang;Hua Chen;Wei Liu;Songjie Yang;Chau Yuen;Hing Cheung So

{"title":"Three-Dimensional Localization of Mixed Near-Field and Far-Field Sources Based on a Unified Exact Propagation Model","authors":"Jiaxiong Fang;Hua Chen;Wei Liu;Songjie Yang;Chau Yuen;Hing Cheung So","doi":"10.1109/TSP.2024.3520551","DOIUrl":"10.1109/TSP.2024.3520551","url":null,"abstract":"In applications like speaker localization using a microphone array, the collected signals are typically a mixture of far-field (FF) and near-field (NF) sources. To find the positions of both NF and FF sources, a three-dimensional spatial-temporal localization algorithm based on a unified exact propagation geometry is developed in this paper, which avoids approximating the spatial phase difference with the first-order and second-order Taylor expansions applied to FF and NF sources, respectively. Our scheme utilizes cross-correlation to produce virtual observations for establishing a third-order parallel factor data model with the use of spatial and temporal information. The array's steering vectors can be extracted by trilinear decomposition. The amplitude and phase information of the whole array elements is jointly exploited to classify the source types and obtain the location estimates via a least squares method. Moreover, the proposed algorithm is computationally efficient since no spectral searches, high-order statistics calculations or parameter pairing procedures are required. The deterministic Cramér-Rao bound is also derived as a performance benchmark, and numerical results are provided to demonstrate the effectiveness of the developed method.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"245-258"},"PeriodicalIF":4.6,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867290","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

IEEE Signal Processing Society Information IEEE信号处理学会信息

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-20 DOI: 10.1109/TSP.2024.3354364

引用次数: 0

List of Reviewers 审稿人名单

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-19 DOI: 10.1109/TSP.2024.3499092

引用次数: 0

Hybrid DTD-AOA Multi-Object Localization in 3-D by Single Receiver Without Synchronization and Some Transmitter Positions: Solutions and Analysis 单接收机无同步和若干发射机位置的混合DTD-AOA三维多目标定位：解决方案与分析

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-17 DOI: 10.1109/TSP.2024.3519442

Danyan Lin;Gang Wang;K. C. Ho;Lei Huang

{"title":"Hybrid DTD-AOA Multi-Object Localization in 3-D by Single Receiver Without Synchronization and Some Transmitter Positions: Solutions and Analysis","authors":"Danyan Lin;Gang Wang;K. C. Ho;Lei Huang","doi":"10.1109/TSP.2024.3519442","DOIUrl":"10.1109/TSP.2024.3519442","url":null,"abstract":"This paper addresses the multi-object localization problem by using a hybrid of differential time delay (DTD) and angle-of-arrival (AOA) measurements collected by a single receiver in an unsynchronized multistatic localization system, where two kinds of transmitters, intentional transmitters at known positions and unintentional transmitters at unknown positions, are used for the illumination of the objects. By integrating the DTD and AOA measurements, we first derive a new set of transformed observation models relating to the object positions, and then investigate the three cases of intentional transmitters only, a mix of intentional and unintentional transmitters, and unintentional transmitters only. Localization for the first case is addressed by a linear weighted least squares (LWLS) estimator and the other two are solved by applying semidefinite relaxation followed with an LWLS estimator. Furthermore, we conduct a thorough theoretical analysis. It shows that incorporating unintentional transmitters at unknown positions is beneficial to improve the localization performance, and increasing the number of objects will also improve the positioning accuracy when unintentional transmitters are used. Additionally, a theoretical bias analysis is conducted, based on which a bias-subtracted solution is given. Both theoretical mean square error analysis and simulations validate well the good performance of the proposed methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"305-323"},"PeriodicalIF":4.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840864","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

Learning Flock: Enhancing Sets of Particles for Multi Substate Particle Filtering With Neural Augmentation 学习群：利用神经增强技术增强多子态粒子过滤的粒子集

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-16 DOI: 10.1109/TSP.2024.3518695

Itai Nuri;Nir Shlezinger

{"title":"Learning Flock: Enhancing Sets of Particles for Multi Substate Particle Filtering With Neural Augmentation","authors":"Itai Nuri;Nir Shlezinger","doi":"10.1109/TSP.2024.3518695","DOIUrl":"10.1109/TSP.2024.3518695","url":null,"abstract":"A leading family of algorithms for state estimation in dynamic systems with multiple sub-states is based on particle filters (PFs). PFs often struggle when operating under complex or approximated modelling (necessitating many particles) with low latency requirements (limiting the number of particles), as is typically the case in multi target tracking (MTT). In this work, we introduce a deep neural network (DNN) augmentation for PFs termed \u0000<italic>learning flock (LF)</i>\u0000. LF learns to correct a particles-weights set, which we coin \u0000<italic>flock</i>\u0000, based on the relationships between all sub-particles in the set itself, while disregarding the set acquisition procedure. Our proposed LF, which can be readily incorporated into different PFs flow, is designed to facilitate rapid operation by maintaining accuracy with a reduced number of particles. We introduce a dedicated training algorithm, allowing both supervised and unsupervised training, and yielding a module that supports a varying number of sub-states and particles without necessitating re-training. We experimentally show the improvements in performance, robustness, and latency of LF augmentation for radar multi-target tracking, as well its ability to mitigate the effect of a mismatched observation modelling. We also compare and illustrate the advantages of LF over a state-of-the-art DNN-aided PF, and demonstrate that LF enhances both classic PFs as well as DNN-based filters.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"99-112"},"PeriodicalIF":4.6,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142832369","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

Kalman Filter for Discrete Processes With Timing Jitter 具有定时抖动的离散过程的卡尔曼滤波器

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-12-13 DOI: 10.1109/TSP.2024.3517158

Oscar G. Ibarra-Manzano;José A. Andrade-Lucio;Miguel A. Vazquez Olguin;Yuriy S. Shmaliy

{"title":"Kalman Filter for Discrete Processes With Timing Jitter","authors":"Oscar G. Ibarra-Manzano;José A. Andrade-Lucio;Miguel A. Vazquez Olguin;Yuriy S. Shmaliy","doi":"10.1109/TSP.2024.3517158","DOIUrl":"10.1109/TSP.2024.3517158","url":null,"abstract":"The sampling interval generated by a local clock (biological, physical, or digital) is known to have a certain amount of errors (deterministic or random) called timing jitter. The latter can vary in nature and magnitude depending on how accurately the time scale is formed and the dynamic process is sampled. In state estimation, timing jitter can cause extra errors that cannot always be ignored. In this paper, we modify the Kalman filter for discrete processes with random timing jitter and call it jitter Kalman filter (JKF). The JKF is developed both intuitively and in the first-order approximation. It is shown that to cope with timing jitter, the system noise covariance acquires an additional term, which is proportional to the fractional jitter standard deviation and the process rate. Based on extensive numerical simulations of polynomial and harmonic models, it is shown that unlimited increase in the process rate leads to the fact that the error caused by jitter also grow without limit. Thus, jitter is dangerous for fast processes, but can be neglected in slow processes. Experimental testing has confirmed the high efficiency of JKF.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"219-229"},"PeriodicalIF":4.6,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142820905","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