IEEE open journal of signal processing最新文献

Robust Estimation of the Covariance Matrix From Data With Outliers 从异常值数据中稳健估计协方差矩阵

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-10-02 DOI: 10.1109/OJSP.2024.3473610

Petre Stoica;Prabhu Babu;Piyush Varshney

{"title":"Robust Estimation of the Covariance Matrix From Data With Outliers","authors":"Petre Stoica;Prabhu Babu;Piyush Varshney","doi":"10.1109/OJSP.2024.3473610","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3473610","url":null,"abstract":"The robust estimation of the covariance matrix is a frequent task in practical applications in which, more often than not, some data samples are outliers. There are several methods that can be used to robustly estimate a covariance matrix from corrupted data, a representative example of which is the \u0000<bold>m\u0000inimum \u0000<bold>c\u0000ovariance \u0000<bold>d\u0000eterminant (MCD) method. In this paper we present a maximum conditional likelihood interpretation of MCD that provides a new motivation of as well as further insights into this method. To perform at its best MCD requires information on the number of outliers in the data, which usually is not available. We propose two new methods for covariance matrix estimation from data with outliers that do not suffer from this problem: TEST (multiple-hypothesis \u0000<bold>test\u0000ing method) which uses the FDR (false discovery rate) to test a set of model hypotheses and hence estimate the number of outliers and their locations, and LIKE (penalized \u0000<bold>like\u0000lihood method) that solves the outlier estimation problem using a GIC (generalized information criterion) to penalize the complexity of a high-dimensional data model. We show by means of numerical simulations that the performances of TEST and LIKE are relatively similar to one another as well as to the performance of the oracle MCD (which uses the true number of outliers) and significantly better than the performance of MCD that uses an upper bound on the outlier number.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1061-1072"},"PeriodicalIF":2.9,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Sensor Placement Based on Sampling Theory for Graph Signals 基于图形信号采样理论的动态传感器布局

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-09-23 DOI: 10.1109/OJSP.2024.3466133

Saki Nomura;Junya Hara;Hiroshi Higashi;Yuichi Tanaka

{"title":"Dynamic Sensor Placement Based on Sampling Theory for Graph Signals","authors":"Saki Nomura;Junya Hara;Hiroshi Higashi;Yuichi Tanaka","doi":"10.1109/OJSP.2024.3466133","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3466133","url":null,"abstract":"In this paper, we consider a sensor placement problem where sensors can move within a network over time. Sensor placement problem aims to select \u0000<inline-formula><tex-math>$K$</tex-math></inline-formula>\u0000 sensor positions from \u0000<inline-formula><tex-math>$N$</tex-math></inline-formula>\u0000 candidates where \u0000<inline-formula><tex-math>$K < N$</tex-math></inline-formula>\u0000. Most existing methods assume that sensor positions are static, i.e., they do not move, however, many mobile sensors like drones, robots, and vehicles can change their positions over time. Moreover, underlying measurement conditions could also be changed, which are difficult to cover with statically placed sensors. We tackle the problem by allowing the sensors to change their positions in their neighbors on the network. We dynamically determine the sensor positions based on graph signal sampling theory such that the non-observed signals on the network can be best recovered from the observations. For signal recovery, the dictionary is learned from a pool of observed signals. It is also used for the sensor position selection. In experiments, we validate the effectiveness of the proposed method via the mean squared error of the reconstructed signals. The proposed dynamic sensor placement outperforms the existing static ones for both synthetic and real data.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1042-1051"},"PeriodicalIF":2.9,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10689379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adversarial Training for Jamming-Robust Channel Estimation in OFDM Systems 用于 OFDM 系统中干扰-稳健信道估计的对抗训练

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-09-02 DOI: 10.1109/OJSP.2024.3453176

Marcele O. K. Mendonça;Paulo S. R. Diniz;Javier Maroto Morales;Pascal Frossard

{"title":"Adversarial Training for Jamming-Robust Channel Estimation in OFDM Systems","authors":"Marcele O. K. Mendonça;Paulo S. R. Diniz;Javier Maroto Morales;Pascal Frossard","doi":"10.1109/OJSP.2024.3453176","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3453176","url":null,"abstract":"Orthogonal frequency-division multiplexing (OFDM) is widely used to mitigate inter-symbol interference (ISI) from multipath fading. However, the open nature of wireless OFDM systems makes them vulnerable to jamming attacks. In this context, pilot jamming is critical as it focuses on corrupting the symbols used for channel estimation and equalization, degrading the system performance. Although neural networks (NNs) can improve channel estimation and mitigate pilot jamming penalty, they are also themselves susceptible to malicious perturbations known as adversarial examples. If the jamming attack is crafted in order to fool the NN, it represents an adversarial example that impairs the proper behavior of OFDM systems. In this work, we explore two machine learning (ML)-based jamming strategies that are especially intended to degrade the performance of ML-based channel estimators, in addition to a traditional Additive White Gaussian Noise (AWGN) jamming attack. These ML-based attacks create noise patterns designed to reduce the precision of the channel estimation process, thereby compromising the reliability and robustness of the communication system. We highlight the vulnerabilities of wireless communication systems to ML-based pilot jamming attacks that corrupts symbols used for channel estimation, leading to system performance degradation. To mitigate these threats, this paper proposes an adversarial training defense mechanism desined to counter jamming attacks. The effectiveness of this defense is validated through simulation results, demonstrating improved channel estimation performance in the presence of jamming attacks. The proposed defense methods aim to enhance the resilience of OFDM systems against pilot jamming attacks, ensuring more robust communication in wireless environments.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1031-1041"},"PeriodicalIF":2.9,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Track Coalescence and Repulsion in Multitarget Tracking: An Analysis of MHT, JPDA, and Belief Propagation Methods 多目标跟踪中的轨迹聚合和排斥：MHT、JPDA 和信念传播方法分析

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-08-28 DOI: 10.1109/OJSP.2024.3451167

Thomas Kropfreiter;Florian Meyer;David F. Crouse;Stefano Coraluppi;Franz Hlawatsch;Peter Willett

{"title":"Track Coalescence and Repulsion in Multitarget Tracking: An Analysis of MHT, JPDA, and Belief Propagation Methods","authors":"Thomas Kropfreiter;Florian Meyer;David F. Crouse;Stefano Coraluppi;Franz Hlawatsch;Peter Willett","doi":"10.1109/OJSP.2024.3451167","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3451167","url":null,"abstract":"Joint probabilistic data association (JPDA) filter methods and multiple hypothesis tracking (MHT) methods are widely used for multitarget tracking (MTT). However, they are known to exhibit undesirable behavior in tracking scenarios with targets in close proximity: JPDA filter methods suffer from the track coalescence effect, i.e., the estimated tracks of targets in close proximity tend to merge and can become indistinguishable, while MHT methods suffer from an opposite effect known as track repulsion, i.e., the estimated tracks of targets in close proximity tend to repel each other in the sense that their separation is larger than the actual distance between the targets. In this paper, we review the JPDA filter and MHT methods and discuss the track coalescence and track repulsion effects. We also consider a more recent methodology for MTT that is based on the belief propagation (BP) algorithm. We argue that BP-based MTT does not exhibit track repulsion because it is not based on maximum a posteriori estimation, and that it exhibits significantly reduced track coalescence because certain properties of the BP messages related to data association encourage separation of target state estimates. Our theoretical arguments are confirmed by numerical results for four representative simulation scenarios.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1089-1106"},"PeriodicalIF":2.9,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10654568","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impact of Varying Distance-Based Fingerprint Similarity Metrics on Affinity Propagation Clustering Performance in Received Signal Strength-Based Fingerprint Databases 基于接收信号强度的指纹数据库中不同距离指纹相似度指标对亲缘传播聚类性能的影响

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-08-26 DOI: 10.1109/OJSP.2024.3449816

Abdulmalik Shehu Yaro;Filip Maly;Karel Maly;Pavel Prazak

{"title":"Impact of Varying Distance-Based Fingerprint Similarity Metrics on Affinity Propagation Clustering Performance in Received Signal Strength-Based Fingerprint Databases","authors":"Abdulmalik Shehu Yaro;Filip Maly;Karel Maly;Pavel Prazak","doi":"10.1109/OJSP.2024.3449816","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3449816","url":null,"abstract":"The affinity propagation clustering (APC) algorithm is popular for fingerprint database clustering because it can cluster without pre-defining the number of clusters. However, the clustering performance of the APC algorithm heavily depends on the chosen fingerprint similarity metric, with distance-based metrics being the most commonly used. Despite its popularity, the APC algorithm lacks comprehensive research on how distance-based metrics affect clustering performance. This emphasizes the need for a better understanding of how these metrics influence its clustering performance, particularly in fingerprint databases. This paper investigates the impact of various distance-based fingerprint similarity metrics on the clustering performance of the APC algorithm. It identifies the best fingerprint similarity metric for optimal clustering performance for a given fingerprint database. The analysis is conducted across five experimentally generated online fingerprint databases, utilizing seven distance-based metrics: Euclidean, squared Euclidean, Manhattan, Spearman, cosine, Canberra, and Chebyshev distances. Using the silhouette score as the performance metric, the simulation results indicate that structural characteristics of the fingerprint database, such as the distribution of fingerprint vectors, play a key role in selecting the best fingerprint similarity metric. However, Euclidean and Manhattan distances are generally the preferable choices for use as fingerprint similarity metrics for the APC algorithm across most fingerprint databases, regardless of their structural characteristics. It is recommended that other factors, such as computational intensity and the presence or absence of outliers, be considered alongside the structural characteristics of the fingerprint database when choosing the appropriate fingerprint similarity metric for maximum clustering performance.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1005-1014"},"PeriodicalIF":2.9,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10646489","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142276495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image Detection Using Combinatorial Auction 利用组合拍卖进行图像检测

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-08-15 DOI: 10.1109/OJSP.2024.3444717

Simon Anuk;Tamir Bendory;Amichai Painsky

引用次数: 0

Multilinear Kernel Regression and Imputation via Manifold Learning 多线性核回归和通过 Manifold Learning 进行推算

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-08-15 DOI: 10.1109/OJSP.2024.3444707

Duc Thien Nguyen;Konstantinos Slavakis

{"title":"Multilinear Kernel Regression and Imputation via Manifold Learning","authors":"Duc Thien Nguyen;Konstantinos Slavakis","doi":"10.1109/OJSP.2024.3444707","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3444707","url":null,"abstract":"This paper introduces a novel kernel regression framework for data imputation, coined multilinear kernel regression and imputation via the manifold assumption (MultiL-KRIM). Motivated by manifold learning, MultiL-KRIM models data features as a point-cloud located in or close to a user-unknown smooth manifold embedded in a reproducing kernel Hilbert space. Unlike typical manifold-learning routes, which seek low-dimensional patterns via regularizers based on graph-Laplacian matrices, MultiL-KRIM builds instead on the intuitive concept of tangent spaces to manifolds and incorporates collaboration among point-cloud neighbors (regressors) directly into the data-modeling term of the loss function. Multiple kernel functions are allowed to offer robustness and rich approximation properties, while multiple matrix factors offer low-rank modeling, dimensionality reduction and streamlined computations, with no need of training data. Two important application domains showcase the functionality of MultiL-KRIM: time-varying-graph-signal (TVGS) recovery, and reconstruction of highly accelerated dynamic-magnetic-resonance-imaging (dMRI) data. Extensive numerical tests on real TVGS and synthetic dMRI data demonstrate that the “shallow” MultiL-KRIM offers remarkable speedups over its predecessors and outperforms other “shallow” state-of-the-art techniques, with a more intuitive and explainable pipeline than deep-image-prior methods.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1073-1088"},"PeriodicalIF":2.9,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10637724","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Occlusion-Informed Radar Detection for Millimeter-Wave Indoor Sensing 毫米波室内传感的遮挡感应雷达探测

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-08-15 DOI: 10.1109/OJSP.2024.3444709

Ahmed Murtada;Bhavani Shankar Mysore Rama Rao;Moein Ahmadi;Udo Schroeder

{"title":"Occlusion-Informed Radar Detection for Millimeter-Wave Indoor Sensing","authors":"Ahmed Murtada;Bhavani Shankar Mysore Rama Rao;Moein Ahmadi;Udo Schroeder","doi":"10.1109/OJSP.2024.3444709","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3444709","url":null,"abstract":"The emergence of Multiple-Input Multiple-Output (MIMO) millimeter-wave (mmWave) radar sensors has prompted interest in indoor sensing applications, including human detection, vital signs monitoring, and real-time tracking in crowded environments. These sensors, equipped with multiple antenna elements, offer high angular resolution, often referred to as imaging radars for their capability to detect high-resolution point clouds. Employing radar systems with high-angular resolution in occlusion-prone scenarios often results in sparse signal returns in range profiles. In extreme cases, only one target return may be observed, as the resolution grid size becomes significantly smaller than the targets, causing portions of the targets to consistently occupy the full area of a test cell. Leveraging this structure, we propose two detectors to enhance the detection of non-occluded targets in such scenarios, thereby providing accurate high-resolution point clouds. The first method employs multiple hypothesis testing over each range profile where the range cells within are considered mutually occluding. The second is formulated based on binary hypothesis testing for each cell, considering the distribution of the signal in the other cells within the same range profile. Numerical analysis demonstrates the superior performance of the latter method over both the classic detection and the former method, especially in low Signal-to-Noise Ratio (SNR) scenarios. Our work showcases the potential of occlusion-informed detection in imaging radars to improve the detection probability of non-occluded targets and reduce false alarms in challenging indoor environments.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"976-990"},"PeriodicalIF":2.9,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10637692","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Signal Processing Society Information 电气和电子工程师学会信号处理学会信息

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-08-14 DOI: 10.1109/OJSP.2023.3347994

引用次数: 0

Self-Supervised Learning-Based Time Series Classification via Hierarchical Sparse Convolutional Masked-Autoencoder 通过层次稀疏卷积掩码自动编码器进行基于自监督学习的时间序列分类

IF 2.9

IEEE open journal of signal processing Pub Date : 2024-07-30 DOI: 10.1109/OJSP.2024.3435673

Ting Yu;Kele Xu;Xu Wang;Bo Ding;Dawei Feng

{"title":"Self-Supervised Learning-Based Time Series Classification via Hierarchical Sparse Convolutional Masked-Autoencoder","authors":"Ting Yu;Kele Xu;Xu Wang;Bo Ding;Dawei Feng","doi":"10.1109/OJSP.2024.3435673","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3435673","url":null,"abstract":"In recent years, the use of time series analysis has become widespread, prompting researchers to explore methods to improve classification. Time series self-supervised learning has emerged as a significant area of study, aiming to uncover patterns in unlabeled data for richer information. Contrastive self-supervised learning, particularly, has gained attention for time series classification. However, it introduces inductive bias by generating positive and negative samples. Another approach involves Masked Autoencoders (MAE), which are effective for various data types. However, due to their reliance on the Transformer architecture, they demand significant computational resources during the pre-training phase. Recently, inspired by the remarkable advancements achieved by convolutional networks in the domain of time series forecasting, we aspire to employ convolutional networks utilizing a strategy of mask recovery for pre-training time series models. This study introduces a novel model termed Hierarchical Sparse Convolutional Masked-Autoencoder, “HSC-MAE”, which seamlessly integrates convolutional operations with the MAE architecture to adeptly capture time series features across varying scales. Furthermore, the HSC-MAE model incorporates dedicated decoders that amalgamate global and local information, enhancing its capacity to comprehend intricate temporal patterns. To gauge the effectiveness of the proposed approach, an extensive array of experiments was conducted across nine distinct datasets. The experimental outcomes stand as a testament to the efficacy of HSC-MAE in effectively mitigating the aforementioned challenges.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"964-975"},"PeriodicalIF":2.9,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614789","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142099791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0