Signal Processing最新文献_第8页

Variational resampling-free cubature Kalman filter for GNSS/INS with measurement outlier detection 测量离群点检测GNSS/INS的无重采样变分培养卡尔曼滤波

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-27 DOI: 10.1016/j.sigpro.2025.110036

Bingbo Cui , Wu Chen , Duojie Weng , Jingxian Wang , Xinhua Wei , Yongyun Zhu

{"title":"Variational resampling-free cubature Kalman filter for GNSS/INS with measurement outlier detection","authors":"Bingbo Cui , Wu Chen , Duojie Weng , Jingxian Wang , Xinhua Wei , Yongyun Zhu","doi":"10.1016/j.sigpro.2025.110036","DOIUrl":"10.1016/j.sigpro.2025.110036","url":null,"abstract":"<div><div>In the information fusion of GNSS/INS, the cubature Kalman filter (CKF) has been widely recognized for its ability to map the probability distributions more accurately than the extended Kalman filter. The resampling-free sigma-point update framework (SUF) propagates additional information based on the residuals of instantiated points from nonlinear transforms, which approximates the covariance of the posterior state more effectively than resampling-based SUF. Unfortunately, resampling-free SUF inherits the limitations of the KF framework, where measurement outliers caused by GNSS signal blocking and disturbances significantly degrade its performance. In this paper, a variational-based SUF is proposed for GNSS/INS information fusion, in which the measurement noise covariance and outlier indicator are iteratively updated using variational Bayesian inference. Consequently, an adaptive SUF is proposed based on outlier-dependent switching SUFs, leading to the development of a variational resampling-free CKF. Numerical simulations and a car-mounted GNSS/INS field test were conducted to demonstrate the effectiveness of the proposed algorithm. The results indicate that the proposed algorithm can efficiently address measurement outliers and time-varying measurement noise.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"237 ","pages":"Article 110036"},"PeriodicalIF":3.4,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888134","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

Frequency–phase coupled parameter estimation for vibration measurement with LFMCW radar LFMCW雷达振动测量的频相耦合参数估计

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-26 DOI: 10.1016/j.sigpro.2025.110033

Xuyao Yu , Zijun Gong , Zhilu Lai

{"title":"Frequency–phase coupled parameter estimation for vibration measurement with LFMCW radar","authors":"Xuyao Yu , Zijun Gong , Zhilu Lai","doi":"10.1016/j.sigpro.2025.110033","DOIUrl":"10.1016/j.sigpro.2025.110033","url":null,"abstract":"<div><div>Linear frequency modulated continuous wave (LFMCW) radar is widely employed in vibration measurement. In the received intermediate frequency signal, the distance information is embedded in both the frequency and phase, i.e., frequency–phase coupling. Early studies rely primarily on classical phase unwrapping algorithms, which frequently fail during rapid or large-amplitude vibrations. Although recent advances have enhanced robustness by incorporating frequency-derived coarse distance estimates as side information for phase unwrapping, these sequential approaches still under-utilize the inherent frequency–phase coupling relationship. In this article, we propose a novel method that jointly leverages both frequency and phase information for direct distance estimation. To start with, we derive a simplified discrete-time baseband signal model, which clearly unveils the coupling. The maximum likelihood (ML) estimation is used to reap the promised performance, but its complexity grows exponentially with the number of observations. To address the complexity barrier, we propose a heuristic method to sequentially solve such a joint optimization problem, and the performance is close to the Cramér–Rao lower bound (CRLB). We analyze the behavior of the ML estimators and discuss the impact of various parameters on system performance. A real-world experiment validates the system model and the proposed algorithm, with results aligning with theoretical analysis.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"237 ","pages":"Article 110033"},"PeriodicalIF":3.4,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903453","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

Robust inference with incompleteness for logistic regression model 逻辑回归模型的不完备性鲁棒推理

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-25 DOI: 10.1016/j.sigpro.2025.110027

M. Cherifi , M.N. El Korso , S. Fortunati , A. Mesloub , L. Ferro-Famil

引用次数: 0

Fixed-point fully adaptive interpolated Volterra filter under recursive maximum correntropy 递归最大熵下的不动点全自适应插值Volterra滤波器

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-24 DOI: 10.1016/j.sigpro.2025.110055

Tao Deng , Lu Lu , Tao Lei , Badong Chen

引用次数: 0

Multi-Object Tracking based on Optimal Transport and Coordinate Attention Mechanism 基于最优运输和协调注意机制的多目标跟踪

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-24 DOI: 10.1016/j.sigpro.2025.110058

Wenjuan Shi, Xiangwei Zheng, Lifeng Zhang, Cun Ji, Yuang Zhang, Ji Bian

{"title":"Multi-Object Tracking based on Optimal Transport and Coordinate Attention Mechanism","authors":"Wenjuan Shi, Xiangwei Zheng, Lifeng Zhang, Cun Ji, Yuang Zhang, Ji Bian","doi":"10.1016/j.sigpro.2025.110058","DOIUrl":"10.1016/j.sigpro.2025.110058","url":null,"abstract":"<div><div>Multi-Object Tracking (MOT) has currently attracted significant interest due to its wide applications in various fields, such as autonomous driving, intelligent surveillance, and behavior recognition. However, appearance similarity of different objects results in low accuracy of target matching and difficulties in data association. In this paper, we propose a Multi-Object Tracking based on Optimal Transport and Coordinate Attention Mechanism (MOT2A), which addresses above challenges by integrating the attention mechanism with optimal transport. These strategies effectively enhance the extraction of discriminative appearance features and improve target matching between different frames. Firstly, we construct a novel Coordinate attention module (CASA), which models the interdependence between the channel domain and the spatial domain of the feature map. Secondly, a Triplet loss with optimal transport (SK-Triplet) is designed to adjust the distance matrix for effective clustering of positive and negative samples during loss calculation. Finally, extensive experiments are conducted on MOT17 and MOT20. For MOT17: 79.4 MOTA, 78.9 IDF1, and 63.9 HOTA; For MOT20: 77.0 MOTA, 76.3 IDF1, and 62.3 HOTA are achieved, respectively. Compared to existing MOT methods, our method shows significant improvements in accuracy and stability. The code is available at: <span><span>https://github.com/420-s/MOT2A</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"236 ","pages":"Article 110058"},"PeriodicalIF":3.4,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877230","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

Aggregatedf-average neural network applied to few-shot class incremental learning 聚合平均神经网络在小样本增量学习中的应用

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-23 DOI: 10.1016/j.sigpro.2025.110054

Mathieu Vu , Émilie Chouzenoux , Ismail Ben Ayed , Jean-Christophe Pesquet

引用次数: 0

Outlier-resistant Bayesian tensor completion for angle estimation in bistatic MIMO radar under array element failures 阵列元失效情况下双基地MIMO雷达角度估计的抗离群贝叶斯张量补全

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-21 DOI: 10.1016/j.sigpro.2025.110061

Yang Song, Jincan Zhang, Jinli Chen, Gangyi Tu, Jiaqiang Li

{"title":"Outlier-resistant Bayesian tensor completion for angle estimation in bistatic MIMO radar under array element failures","authors":"Yang Song, Jincan Zhang, Jinli Chen, Gangyi Tu, Jiaqiang Li","doi":"10.1016/j.sigpro.2025.110061","DOIUrl":"10.1016/j.sigpro.2025.110061","url":null,"abstract":"<div><div>Conventional angle estimation algorithms for multiple-input multiple-output (MIMO) radar are susceptible to array element failures and impulsive noise, which makes achieving accurate estimates in practical applications challenging. To remedy this, we propose an outlier-resistant Bayesian tensor completion algorithm for joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation in bistatic MIMO radar under element failures and impulsive noise. First, we constructed a slice-missing tensor signal model that is corrupted by outliers. To achieve better low-rank regularization on this tensor, we convert it into a structured tensor with randomly missing entries. We then design an outlier-resistant Bayesian tensor completion model, which accounts for array element failures and the \"heavy-tailed\" nature of impulsive noise. In the proposed model, the reconstruction of missing entries represents array element failures, while Student-t distribution models the impulsive noise in the measurements. A variational Bayesian inference scheme is developed to address the proposed model, which alternates among estimating the factor matrices, recovering the tensor rank, and mitigating impulsive noise. Finally, the completed factor matrix is used to extract DODs and DOAs using the shift invariance technique. Simulation results confirm the outstanding performance of the proposed algorithm in estimating target numbers and angles under element failures and impulsive noise.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"236 ","pages":"Article 110061"},"PeriodicalIF":3.4,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873732","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 atrial fibrillation signals analysis algorithm in line with clinical diagnostic criteria 一种符合临床诊断标准的房颤信号分析算法

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-19 DOI: 10.1016/j.sigpro.2025.110068

Yongjian Li , Meng Chen , Xinliang Qu , Baokun Han , Lei Liu , Shoushui Wei

{"title":"An atrial fibrillation signals analysis algorithm in line with clinical diagnostic criteria","authors":"Yongjian Li , Meng Chen , Xinliang Qu , Baokun Han , Lei Liu , Shoushui Wei","doi":"10.1016/j.sigpro.2025.110068","DOIUrl":"10.1016/j.sigpro.2025.110068","url":null,"abstract":"<div><div>The detection of atrial fibrillation using deep learning techniques is a hot topic in the field of signal processing. However, simply stacking modules to pursue accuracy, or compressing inputs and parameters to pursue real-time performance, leads to gambling problem between information redundancy and information loss in deep learning algorithms. At the same time, the features obtained by deep learning lack interpretability. Therefore, this study proposes a T neural network (T-Net) that integrates feature extraction, selection, and fusion. In T-Net, horizontal path extracts multi-scale information of electrocardiograms through multi-level feature reuse, feature filter embeds attention mechanism and voting algorithm internally to select information flow, and vertical path uses channel-wise point-to-point weighting to capture the nonlinear relationships of multi-scale information. Through pre-training and fine-tuning on the MIT-BIH atrial fibrillation database consisting of 23 patients, and testing on the Shandong Provincial Hospital database consisting of 252 patients, T-Net achieved accuracy, specificity, sensitivity, and F1 score of 97.95 %, 97.01 %, 98.89 %, and 97.97 %, respectively. T-Net addresses the gambling problem between information redundancy and information insufficiency, and the extracted features demonstrate good interpretability consistent with clinical diagnostic criteria, showing promising clinical application prospects.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"236 ","pages":"Article 110068"},"PeriodicalIF":3.4,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860442","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

Multiple-model coding scheme for electrical signal compression 电信号压缩的多模型编码方案

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-18 DOI: 10.1016/j.sigpro.2025.110037

Corentin Presvôts , Michel Kieffer , Thibault Prevost , Patrick Panciatici , Zuxing Li , Pablo Piantanida

引用次数: 0

Robust mixture filtering block based on logarithmic Student’s t-based criterion 基于对数Student准则的鲁棒混合滤波块

IF 3.4 2区工程技术

Signal Processing Pub Date : 2025-04-17 DOI: 10.1016/j.sigpro.2025.110044

Mingjing Cui , Yunxiang Jiang , Dongyuan Lin , Yunfei Zheng , Shiyuan Wang

{"title":"Robust mixture filtering block based on logarithmic Student’s t-based criterion","authors":"Mingjing Cui , Yunxiang Jiang , Dongyuan Lin , Yunfei Zheng , Shiyuan Wang","doi":"10.1016/j.sigpro.2025.110044","DOIUrl":"10.1016/j.sigpro.2025.110044","url":null,"abstract":"<div><div>Determining an appropriate cost function is crucial to develop adaptive filters. However, current robust algorithms may not be capable of satisfying the requirements of various non-Gaussian environments due to their limited performance surfaces and gradient relationships. To this end, a novel and robust cost function called logarithmic Student’s <span><math><mi>t</mi></math></span>-based (logST) criterion using Student’s <span><math><mi>t</mi></math></span> distribution is first proposed in this paper. Owing to its excellent properties in the algorithm generalization, robust gradient relationship, and efficient performance surface, the proposed logST algorithm achieves filtering accuracy improvement in both Gaussian and non-Gaussian environments. To further enhance the convergence performance and tracking capability in nonlinear system identification, a novel nonlinear block-oriented framework is constructed using the mixture of original space and random Fourier features space. Then, a recursive method is applied to achieve optimization solution in this nonlinear block-oriented framework, generating the mixture random Fourier features recursive logST (MRFFRlogST) algorithm. Finally, simulations on linear and nonlinear system identifications, as well as Chua’s time-series prediction under various noise environments validate the superiorities of logST and MRFFRlogST.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"236 ","pages":"Article 110044"},"PeriodicalIF":3.4,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850615","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