Pub Date : 2024-11-11DOI: 10.1109/OJSP.2024.3495553
Jinho Choi
In order to extract governing equations from time-series data, various approaches are proposed. Among those, sparse identification of nonlinear dynamics (SINDy) stands out as a successful method capable of modeling governing equations with a minimal number of terms, utilizing the principles of compressive sensing. This feature, which relies on a small number of terms, is crucial for interpretability. The effectiveness of SINDy hinges on the choice of candidate functions within its dictionary to extract governing equations of dynamical systems. A larger dictionary allows for more terms, enhancing the quality of approximations. However, the computational complexity scales with dictionary size, rendering SINDy less suitable for high-dimensional datasets, even though it has been successfully applied to low-dimensional datasets. To address this challenge, we introduce iterative SINDy in this paper, where the dictionary undergoes expansion and compression through iterations. We also conduct an analysis of the convergence properties of iterative SINDy. Simulation results validate that iterative SINDy can achieve nearly identical performance to SINDy, while significantly reducing computational complexity. Notably, iterative SINDy demonstrates effectiveness with high-dimensional time-series data without incurring the prohibitively high computational cost associated with SINDy.
{"title":"Iterative Sparse Identification of Nonlinear Dynamics","authors":"Jinho Choi","doi":"10.1109/OJSP.2024.3495553","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3495553","url":null,"abstract":"In order to extract governing equations from time-series data, various approaches are proposed. Among those, sparse identification of nonlinear dynamics (SINDy) stands out as a successful method capable of modeling governing equations with a minimal number of terms, utilizing the principles of compressive sensing. This feature, which relies on a small number of terms, is crucial for interpretability. The effectiveness of SINDy hinges on the choice of candidate functions within its dictionary to extract governing equations of dynamical systems. A larger dictionary allows for more terms, enhancing the quality of approximations. However, the computational complexity scales with dictionary size, rendering SINDy less suitable for high-dimensional datasets, even though it has been successfully applied to low-dimensional datasets. To address this challenge, we introduce iterative SINDy in this paper, where the dictionary undergoes expansion and compression through iterations. We also conduct an analysis of the convergence properties of iterative SINDy. Simulation results validate that iterative SINDy can achieve nearly identical performance to SINDy, while significantly reducing computational complexity. Notably, iterative SINDy demonstrates effectiveness with high-dimensional time-series data without incurring the prohibitively high computational cost associated with SINDy.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1107-1118"},"PeriodicalIF":2.9,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691822","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}
Pub Date : 2024-10-02DOI: 10.1109/OJSP.2024.3473610
Petre Stoica;Prabhu Babu;Piyush Varshney
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 m inimum c ovariance d eterminant (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 test ing 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 like lihood 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.
{"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</b>\u0000inimum \u0000<bold>c</b>\u0000ovariance \u0000<bold>d</b>\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</b>\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</b>\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}
In this paper, we consider a sensor placement problem where sensors can move within a network over time. Sensor placement problem aims to select $K$ sensor positions from $N$ candidates where $K < N$ . 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.
{"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}
Pub Date : 2024-09-02DOI: 10.1109/OJSP.2024.3453176
Marcele O. K. Mendonça;Paulo S. R. Diniz;Javier Maroto Morales;Pascal Frossard
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.
正交频分复用（OFDM）被广泛用于缓解多径衰落造成的符号间干扰（ISI）。然而，无线 OFDM 系统的开放性使其容易受到干扰攻击。在这种情况下，先导干扰至关重要，因为它主要会破坏用于信道估计和均衡的符号，从而降低系统性能。虽然神经网络（NN）可以改善信道估计并减轻先导干扰的惩罚，但它们本身也容易受到被称为对抗范例的恶意扰动的影响。如果干扰攻击是为了愚弄神经网络而精心设计的，那么它就代表了一种损害 OFDM 系统正常行为的对抗范例。在这项工作中，除了传统的加性白高斯噪声（AWGN）干扰攻击外，我们还探索了两种基于机器学习（ML）的干扰策略，其目的是降低基于 ML 的信道估计器的性能。这些基于 ML 的攻击会产生噪音模式，旨在降低信道估计过程的精度，从而损害通信系统的可靠性和鲁棒性。我们强调了无线通信系统在基于 ML 的先导干扰攻击面前的脆弱性，这种攻击会破坏用于信道估计的符号，从而导致系统性能下降。为了减轻这些威胁，本文提出了一种对抗性训练防御机制，旨在对抗干扰攻击。这种防御机制的有效性通过仿真结果得到了验证，证明了在存在干扰攻击的情况下信道估计性能的提高。所提出的防御方法旨在增强 OFDM 系统抵御先导干扰攻击的能力，确保在无线环境中实现更稳健的通信。
{"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}
Pub Date : 2024-08-28DOI: 10.1109/OJSP.2024.3451167
Thomas Kropfreiter;Florian Meyer;David F. Crouse;Stefano Coraluppi;Franz Hlawatsch;Peter Willett
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.
联合概率数据关联（JPDA）滤波方法和多重假设跟踪（MHT）方法被广泛用于多目标跟踪（MTT）。然而，众所周知，在目标距离很近的跟踪场景中，它们会表现出不理想的行为：JPDA 滤波方法会受到轨迹凝聚效应的影响，即近距离目标的估计轨迹趋于合并，变得难以区分；而 MHT 方法则会受到称为轨迹排斥的相反效应的影响，即近距离目标的估计轨迹趋于相互排斥，它们之间的距离大于目标之间的实际距离。本文回顾了 JPDA 滤波和 MHT 方法，并讨论了轨迹凝聚和轨迹排斥效应。我们还考虑了一种基于信念传播（BP）算法的最新 MTT 方法。我们认为，基于 BP 算法的 MTT 不会出现轨迹排斥现象，因为它不是基于最大后验估计，而且由于与数据关联相关的 BP 信息的某些特性会鼓励目标状态估计的分离，因此它能显著减少轨迹凝聚。我们的理论论点得到了四种代表性模拟场景的数值结果的证实。
{"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}
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.
{"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}
Pub Date : 2024-08-15DOI: 10.1109/OJSP.2024.3444717
Simon Anuk;Tamir Bendory;Amichai Painsky
This paper studies the optimal solution of the classical problem of detecting the location of multiple image occurrences in a two-dimensional, noisy measurement. Assuming the image occurrences do not overlap, we formulate this task as a constrained maximum likelihood optimization problem. We show that the maximum likelihood estimator is equivalent to an instance of the winner determination problem from the field of combinatorial auction and that the solution can be obtained by searching over a binary tree. We then design a pruning mechanism that significantly accelerates the runtime of the search. We demonstrate on simulations and electron microscopy data sets that the proposed algorithm provides accurate detection in challenging regimes of high noise levels and densely packed image occurrences.
{"title":"Image Detection Using Combinatorial Auction","authors":"Simon Anuk;Tamir Bendory;Amichai Painsky","doi":"10.1109/OJSP.2024.3444717","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3444717","url":null,"abstract":"This paper studies the optimal solution of the classical problem of detecting the location of multiple image occurrences in a two-dimensional, noisy measurement. Assuming the image occurrences do not overlap, we formulate this task as a constrained maximum likelihood optimization problem. We show that the maximum likelihood estimator is equivalent to an instance of the winner determination problem from the field of combinatorial auction and that the solution can be obtained by searching over a binary tree. We then design a pruning mechanism that significantly accelerates the runtime of the search. We demonstrate on simulations and electron microscopy data sets that the proposed algorithm provides accurate detection in challenging regimes of high noise levels and densely packed image occurrences.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1015-1022"},"PeriodicalIF":2.9,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10637697","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320494","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}
Pub Date : 2024-08-15DOI: 10.1109/OJSP.2024.3444707
Duc Thien Nguyen;Konstantinos Slavakis
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.
{"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}
Pub Date : 2024-08-15DOI: 10.1109/OJSP.2024.3444709
Ahmed Murtada;Bhavani Shankar Mysore Rama Rao;Moein Ahmadi;Udo Schroeder
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.
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Pub Date : 2024-08-14DOI: 10.1109/OJSP.2023.3347994
{"title":"IEEE Signal Processing Society Information","authors":"","doi":"10.1109/OJSP.2023.3347994","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3347994","url":null,"abstract":"","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"C2-C2"},"PeriodicalIF":2.9,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10636085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985880","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}
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