Pub Date : 2024-11-19DOI: 10.1016/j.sigpro.2024.109787
Han Zou , Sunyong Wu , Qiutiao Xue , Xiyan Sun , Ming Li
For the single-target tracking problem under multi-class noise mixing, the Gaussian-Student’s t-Skew mixture (GSTSM) distribution is proposed by introducing the Dirichlet random variables to model the mixed noise superimposed by multiple noise sources. By introducing multinomial random variables, the GSTSM distribution can be represented within a hierarchical model. This model is subsequently applied to the state–space model, employing a variational Bayesian (VB) approach to propose a novel robust Kalman filter based on the GSTSM distribution (GSTSM-KF). Simulation results show that GSTSM-KF can effectively improve the tracking accuracy in mixed noise scenarios.
{"title":"A novel Gaussian-Student’s t-Skew mixture distribution based Kalman filter","authors":"Han Zou , Sunyong Wu , Qiutiao Xue , Xiyan Sun , Ming Li","doi":"10.1016/j.sigpro.2024.109787","DOIUrl":"10.1016/j.sigpro.2024.109787","url":null,"abstract":"<div><div>For the single-target tracking problem under multi-class noise mixing, the Gaussian-Student’s t-Skew mixture (GSTSM) distribution is proposed by introducing the Dirichlet random variables to model the mixed noise superimposed by multiple noise sources. By introducing multinomial random variables, the GSTSM distribution can be represented within a hierarchical model. This model is subsequently applied to the state–space model, employing a variational Bayesian (VB) approach to propose a novel robust Kalman filter based on the GSTSM distribution (GSTSM-KF). Simulation results show that GSTSM-KF can effectively improve the tracking accuracy in mixed noise scenarios.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"230 ","pages":"Article 109787"},"PeriodicalIF":3.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723273","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}
Pub Date : 2024-11-18DOI: 10.1016/j.sigpro.2024.109784
Ben Niu, Yongbo Zhao, Derui Tang
This paper introduces a novel waveform agile MIMO radar waveform design method based on random arrangement of pulse slices (RAPS), enhancing the conventional Frequency diversity LFM (FD-LFM) transmit beampattern synthesis. We conduct a pioneering analysis of the auto-correlation function characteristics of the existing FD-LFM, revealing theoretical insights into its low range resolution and associated factors. The proposed method significantly enhances range resolution and effectively minimizes range sidelobes by combining RAPS and waveform agility between pulses. The efficacy of this approach is corroborated through comprehensive simulation experiments.
本文介绍了一种基于脉冲片随机排列（RAPS）的新型波形敏捷 MIMO 雷达波形设计方法，该方法增强了传统的频率分集低频调制（FD-LFM）发射波形合成。我们对现有 FD-LFM 的自相关函数特性进行了开创性分析，从理论上揭示了它的低测距分辨率和相关因素。所提出的方法通过结合 RAPS 和脉冲间的波形灵活性，大大提高了测距分辨率，并有效地减少了测距侧晃。综合模拟实验证实了这一方法的有效性。
{"title":"Waveform agile MIMO radar fast waveform design based on random arrangement of pulse slices","authors":"Ben Niu, Yongbo Zhao, Derui Tang","doi":"10.1016/j.sigpro.2024.109784","DOIUrl":"10.1016/j.sigpro.2024.109784","url":null,"abstract":"<div><div>This paper introduces a novel waveform agile MIMO radar waveform design method based on random arrangement of pulse slices (RAPS), enhancing the conventional Frequency diversity LFM (FD-LFM) transmit beampattern synthesis. We conduct a pioneering analysis of the auto-correlation function characteristics of the existing FD-LFM, revealing theoretical insights into its low range resolution and associated factors. The proposed method significantly enhances range resolution and effectively minimizes range sidelobes by combining RAPS and waveform agility between pulses. The efficacy of this approach is corroborated through comprehensive simulation experiments.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"230 ","pages":"Article 109784"},"PeriodicalIF":3.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723284","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}
Pub Date : 2024-11-17DOI: 10.1016/j.sigpro.2024.109803
Tong Wu, Gui-Fu Lu
Multi-view clustering (MVC) has gained widespread attention due to its ability to utilize different features from different views. However, the existing MVC methods fail to fully exploit the consistency across multiple views, leading to information loss. Additionally, the performance of the algorithms is not satisfactory due to the inherent noise in the data. To address the above-mentioned issues, this paper proposes the specific and coupled double consistency multi-view subspace clustering with low-rank tensor learning (SCDCMV) method. Specifically, firstly, we simultaneously incorporate the consistency and specificity of multiple views into self-expressive learning. However, the information within the consistency matrix has not been fully utilized, and there still exists some noise. Then, the obtained consistency matrix is once again integrated into self-expressive learning to obtain a new consistency matrix. Thirdly, we combine the two consistency matrices into a tensor and constrain it using tensor nuclear norm (TNN). Then, under the constraint of TNN, the two consistency matrices mutually reinforce each other, which helps fully utilize the consistency information and reduce the impact of noise, ultimately leading to better clustering results. Ultimately, these three steps constitute a framework that is tackled utilizing the augmented Lagrange multiplier method. The performance of SCDCMV has improved by 55.94 %. Experimental results on different datasets indicate that the SCDCMV algorithm outperforms state-of-the-art algorithms. In other words, these experimental results validate the importance of effectively utilizing consistent information from multiple views while reducing the impact of noise. The code is publicly available at https://github.com/TongWuahpu/SCDCMV.
{"title":"Specific and coupled double consistency multi-view subspace clustering with low-rank tensor learning","authors":"Tong Wu, Gui-Fu Lu","doi":"10.1016/j.sigpro.2024.109803","DOIUrl":"10.1016/j.sigpro.2024.109803","url":null,"abstract":"<div><div>Multi-view clustering (MVC) has gained widespread attention due to its ability to utilize different features from different views. However, the existing MVC methods fail to fully exploit the consistency across multiple views, leading to information loss. Additionally, the performance of the algorithms is not satisfactory due to the inherent noise in the data. To address the above-mentioned issues, this paper proposes the specific and coupled double consistency multi-view subspace clustering with low-rank tensor learning (SCDCMV) method. Specifically, firstly, we simultaneously incorporate the consistency and specificity of multiple views into self-expressive learning. However, the information within the consistency matrix has not been fully utilized, and there still exists some noise. Then, the obtained consistency matrix is once again integrated into self-expressive learning to obtain a new consistency matrix. Thirdly, we combine the two consistency matrices into a tensor and constrain it using tensor nuclear norm (TNN). Then, under the constraint of TNN, the two consistency matrices mutually reinforce each other, which helps fully utilize the consistency information and reduce the impact of noise, ultimately leading to better clustering results. Ultimately, these three steps constitute a framework that is tackled utilizing the augmented Lagrange multiplier method. The performance of SCDCMV has improved by 55.94 %. Experimental results on different datasets indicate that the SCDCMV algorithm outperforms state-of-the-art algorithms. In other words, these experimental results validate the importance of effectively utilizing consistent information from multiple views while reducing the impact of noise. The code is publicly available at <span><span>https://github.com/TongWuahpu/SCDCMV</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"229 ","pages":"Article 109803"},"PeriodicalIF":3.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706976","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}
Pub Date : 2024-11-17DOI: 10.1016/j.sigpro.2024.109808
Tian Zhou Member, IEEE , Weijia Yuan , Maria S. Greco Fellow, IEEE , Fulvio Gini Fellow, IEEE
Gate tracking technology plays a crucial role in the autonomous detection of multi-beam echo sounding. The quality of the tracking algorithm directly impacts the quality of the estimate data. Here, we propose a new method for depth gate tracking in multi-beam echo sounding based on the assumption that historical sounding results are geometrically similar to the current ping results. Considering Hausdorff distance measures how far two subsets of a metric space are from each other. An evaluation method based on the concept of similarity distance that relies on the Modified Hausdorff distance is proposed here to measure the similarity between the detection results of different pings and determine the maximum number of historical pings that can be tracked. Then, this model is combined with the iterative closest point registration algorithm to align the bathymetric results of historical pings to the current ping, eliminating the need for prior registration. The registered data are used to initialize the particle distribution in the tracking particle filter and a distance-weighted importance function is established for each beam. Validation on measured data has shown that the proposed method is effective in tracking seabed topography and providing stable gate tracking results.
{"title":"Depth gate tracking method based on historical sounding results in MBES","authors":"Tian Zhou Member, IEEE , Weijia Yuan , Maria S. Greco Fellow, IEEE , Fulvio Gini Fellow, IEEE","doi":"10.1016/j.sigpro.2024.109808","DOIUrl":"10.1016/j.sigpro.2024.109808","url":null,"abstract":"<div><div>Gate tracking technology plays a crucial role in the autonomous detection of multi-beam echo sounding. The quality of the tracking algorithm directly impacts the quality of the estimate data. Here, we propose a new method for depth gate tracking in multi-beam echo sounding based on the assumption that historical sounding results are geometrically similar to the current ping results. Considering Hausdorff distance measures how far two subsets of a metric space are from each other. An evaluation method based on the concept of similarity distance that relies on the Modified Hausdorff distance is proposed here to measure the similarity between the detection results of different pings and determine the maximum number of historical pings that can be tracked. Then, this model is combined with the iterative closest point registration algorithm to align the bathymetric results of historical pings to the current ping, eliminating the need for prior registration. The registered data are used to initialize the particle distribution in the tracking particle filter and a distance-weighted importance function is established for each beam. Validation on measured data has shown that the proposed method is effective in tracking seabed topography and providing stable gate tracking results.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"230 ","pages":"Article 109808"},"PeriodicalIF":3.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744537","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}
Pub Date : 2024-11-17DOI: 10.1016/j.sigpro.2024.109759
Jie Wang , Lu Lu , Zongsheng Zheng , Kai-Li Yin , Yi Yu , Long Shi
Based on the principle of superposition, the active noise control (ANC) technique can achieve satisfactory noise reduction. The filtered-x least mean square (FxLMS) algorithm has been extensively implemented in the ANC problem but it is easily plunged into instability in impulsive noise scenarios. To ameliorate this disadvantage, benefiting from the reduced computational load of the Euclidean direction search (EDS) algorithm and robustness of the maximum correntropy criterion (MCC), a novel filtered-x EDS-MCC (FxEDS-MCC) algorithm is proposed to attenuate the impulsive interference. The theoretical analysis of the FxEDS-MCC algorithm is based on the rotated method and the Taylor series expansion approximation. Simulations validate the accuracy of the theoretical performance and verify the improved performance of the FxEDS-MCC algorithm in comparison with the existing algorithms.
{"title":"Euclidean direction search algorithm with maximum correntropy criterion for active noise control system","authors":"Jie Wang , Lu Lu , Zongsheng Zheng , Kai-Li Yin , Yi Yu , Long Shi","doi":"10.1016/j.sigpro.2024.109759","DOIUrl":"10.1016/j.sigpro.2024.109759","url":null,"abstract":"<div><div>Based on the principle of superposition, the active noise control (ANC) technique can achieve satisfactory noise reduction. The filtered-x least mean square (FxLMS) algorithm has been extensively implemented in the ANC problem but it is easily plunged into instability in impulsive noise scenarios. To ameliorate this disadvantage, benefiting from the reduced computational load of the Euclidean direction search (EDS) algorithm and robustness of the maximum correntropy criterion (MCC), a novel filtered-x EDS-MCC (FxEDS-MCC) algorithm is proposed to attenuate the impulsive interference. The theoretical analysis of the FxEDS-MCC algorithm is based on the rotated method and the Taylor series expansion approximation. Simulations validate the accuracy of the theoretical performance and verify the improved performance of the FxEDS-MCC algorithm in comparison with the existing algorithms.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"229 ","pages":"Article 109759"},"PeriodicalIF":3.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706973","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}
Pub Date : 2024-11-16DOI: 10.1016/j.sigpro.2024.109806
Ziyou Zhou, Wenhua Chen
In industrial fault diagnosis, data imbalance, missing labels, and cross-condition scenarios increase complexity and challenges. While existing research has made progress in these areas, gaps remain in addressing cross-condition fault diagnosis with imbalanced and incomplete labels. To tackle this, we propose the entropy-weighted manifold alignment (E-WMA) method. First, we use sparse filtering techniques for manifold alignment to extract features with good separability from the source and target domains. Next, we adopt an entropy-weighted maximum mean discrepancy strategy to dynamically adjust sample weights based on label information entropy, which reduces distribution differences and mitigates data imbalance. Finally, we build a softmax regression classifier to train and evaluate the fault diagnosis model using the aligned features and adjusted sample weights, enhancing diagnostic accuracy and robustness. Extensive experiments on wind turbine planetary gearbox and bearing fault datasets validate our approach. The results show that our method effectively addresses cross-condition fault diagnosis amid data imbalance and missing labels. This can lead to more accurate fault detection in real-time operations, minimize unplanned downtime, and significantly reduce maintenance costs in industrial environments.
{"title":"Entropy-weighted manifold-adjusted transfer learning for cross-condition fault diagnosis with imbalanced and missing labels","authors":"Ziyou Zhou, Wenhua Chen","doi":"10.1016/j.sigpro.2024.109806","DOIUrl":"10.1016/j.sigpro.2024.109806","url":null,"abstract":"<div><div>In industrial fault diagnosis, data imbalance, missing labels, and cross-condition scenarios increase complexity and challenges. While existing research has made progress in these areas, gaps remain in addressing cross-condition fault diagnosis with imbalanced and incomplete labels. To tackle this, we propose the entropy-weighted manifold alignment (E-WMA) method. First, we use sparse filtering techniques for manifold alignment to extract features with good separability from the source and target domains. Next, we adopt an entropy-weighted maximum mean discrepancy strategy to dynamically adjust sample weights based on label information entropy, which reduces distribution differences and mitigates data imbalance. Finally, we build a softmax regression classifier to train and evaluate the fault diagnosis model using the aligned features and adjusted sample weights, enhancing diagnostic accuracy and robustness. Extensive experiments on wind turbine planetary gearbox and bearing fault datasets validate our approach. The results show that our method effectively addresses cross-condition fault diagnosis amid data imbalance and missing labels. This can lead to more accurate fault detection in real-time operations, minimize unplanned downtime, and significantly reduce maintenance costs in industrial environments.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"229 ","pages":"Article 109806"},"PeriodicalIF":3.4,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706975","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}
Pub Date : 2024-11-15DOI: 10.1016/j.sigpro.2024.109783
Junxia Ma , Ronghuan Li , Yujie Ma , Jing Chen
A robust identification problem for multi-model systems with time-vary delays is considered in this article, where the linear parameter varying model is employed to present the structure of the multi-model systems. To handle outliers in the collected data, we establish an observation model based on a robust principal component analysis (RPCA) algorithm for low-rank matrix recovery. Construct a high-dimensional information matrix using multi-batch measured data. Although this matrix is typically high-dimensional and low-rank, outliers cause it to become high-dimensional and high-rank. By applying the RPCA algorithm, we restore the information matrix to its low-rank form, thus isolating the pure collected data. This process allows us to select a batch of collected data as information vectors for parameter identification. A Markov chain model is established to describe the correlation between time delays. Given the complexity of optimizing log-likelihood functions directly, we derive the estimation problem of model parameters and time delays under the framework of the expectation maximization (EM) algorithm. Therefore, an EM identification algorithm based on RPCA (RPCA-EM) is derived. A numerical simulation and an example involving a continuous stirred tank reactor verify the effectiveness of the proposed RPCA-EM algorithm.
本文考虑了具有时变延迟的多模型系统的鲁棒识别问题，其中采用了线性参数变化模型来呈现多模型系统的结构。为了处理收集到的数据中的异常值，我们建立了一个基于鲁棒主成分分析（RPCA）算法的观测模型，用于低秩矩阵恢复。利用多批次测量数据构建高维信息矩阵。虽然该矩阵通常是高维低秩的，但异常值会使其变成高维高秩矩阵。通过应用 RPCA 算法，我们将信息矩阵还原为低秩形式，从而分离出纯净的采集数据。通过这一过程，我们可以选择一批采集数据作为参数识别的信息向量。建立马尔科夫链模型来描述时间延迟之间的相关性。考虑到直接优化对数似然函数的复杂性，我们在期望最大化（EM）算法框架下推导出模型参数和时间延迟的估计问题。因此，我们推导出了基于 RPCA 的 EM 识别算法（RPCA-EM）。一个数值模拟和一个涉及连续搅拌罐反应器的实例验证了所提出的 RPCA-EM 算法的有效性。
{"title":"A sparse low-rank matrix recovery strategy to deal with robust identification for multi-model systems with time-varying delays","authors":"Junxia Ma , Ronghuan Li , Yujie Ma , Jing Chen","doi":"10.1016/j.sigpro.2024.109783","DOIUrl":"10.1016/j.sigpro.2024.109783","url":null,"abstract":"<div><div>A robust identification problem for multi-model systems with time-vary delays is considered in this article, where the linear parameter varying model is employed to present the structure of the multi-model systems. To handle outliers in the collected data, we establish an observation model based on a robust principal component analysis (RPCA) algorithm for low-rank matrix recovery. Construct a high-dimensional information matrix using multi-batch measured data. Although this matrix is typically high-dimensional and low-rank, outliers cause it to become high-dimensional and high-rank. By applying the RPCA algorithm, we restore the information matrix to its low-rank form, thus isolating the pure collected data. This process allows us to select a batch of collected data as information vectors for parameter identification. A Markov chain model is established to describe the correlation between time delays. Given the complexity of optimizing log-likelihood functions directly, we derive the estimation problem of model parameters and time delays under the framework of the expectation maximization (EM) algorithm. Therefore, an EM identification algorithm based on RPCA (RPCA-EM) is derived. A numerical simulation and an example involving a continuous stirred tank reactor verify the effectiveness of the proposed RPCA-EM algorithm.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"229 ","pages":"Article 109783"},"PeriodicalIF":3.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706977","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}
Pub Date : 2024-11-12DOI: 10.1016/j.sigpro.2024.109772
Huiwen Xue , Jiwei Wen , Ruichao Li , Xiaoli Luan
This study aims to develop a dual games (DGs) mechanism and implement a temporal difference learning (TDL) approach to address distributed filter design while considering network-induced time-varying topology from individual optimality and global equilibrium perspectives. In a detailed analysis, each filtering node (FN) treats its individual filtering action and exogenous disturbance as opposing elements, striving to determine the optimal policy while accounting for the worst-case scenario. This competition between FN and the disturbance culminates in a zero-sum game. Simultaneously, FN collaborates effectively with its neighbors to achieve consensus estimation, giving rise to a non-zero-sum game. Notably, an error-based filtering action is built to solve challenges posed by DGs. Ultimately, each FN attains its estimation at a minimum cost, and the entire distributed filtering network achieves the consensus estimation at a Nash equilibrium. Moreover, the transition probability correlation matrices (TPCMs) of the time-varying topology are obtained through direct observation of multi-episodes of topological transition trajectories. It has been proved that with a sufficiently ample number of episodes, TPCMs converge to their optimal values when TPs are known as apriori. Finally, a numerical example and an aero-engine system are presented to illustrate the effectiveness and practical potential of the proposed method.
{"title":"Distributed filtering with time-varying topology: A temporal-difference learning approach in dual games","authors":"Huiwen Xue , Jiwei Wen , Ruichao Li , Xiaoli Luan","doi":"10.1016/j.sigpro.2024.109772","DOIUrl":"10.1016/j.sigpro.2024.109772","url":null,"abstract":"<div><div>This study aims to develop a dual games (DGs) mechanism and implement a temporal difference learning (TDL) approach to address distributed filter design while considering network-induced time-varying topology from individual optimality and global equilibrium perspectives. In a detailed analysis, each filtering node (FN) treats its individual filtering action and exogenous disturbance as opposing elements, striving to determine the optimal policy while accounting for the worst-case scenario. This competition between FN and the disturbance culminates in a zero-sum game. Simultaneously, FN collaborates effectively with its neighbors to achieve consensus estimation, giving rise to a non-zero-sum game. Notably, an error-based filtering action is built to solve challenges posed by DGs. Ultimately, each FN attains its estimation at a minimum cost, and the entire distributed filtering network achieves the consensus estimation at a Nash equilibrium. Moreover, the transition probability correlation matrices (TPCMs) of the time-varying topology are obtained through direct observation of multi-episodes of topological transition trajectories. It has been proved that with a sufficiently ample number of episodes, TPCMs converge to their optimal values when TPs are known as apriori. Finally, a numerical example and an aero-engine system are presented to illustrate the effectiveness and practical potential of the proposed method.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"229 ","pages":"Article 109772"},"PeriodicalIF":3.4,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658960","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}
Pub Date : 2024-11-12DOI: 10.1016/j.sigpro.2024.109771
Yuying Mai , Mateen Ashraf , Huiqin Du , Bo Tan
This work considers an integrated sensing and communication system, where a reconfigurable intelligent surface (RIS) is utilized to manage interference and radar signals. The sensors are attached to the RIS to sense multiple targets. A joint design of the base station transmit beamforming and RIS phase shift matrix is proposed to minimize total interference and maximize the worst received signal power at the RIS sensors. Due to highly coupled transmit beamforming and RIS phase matrices, the optimization problem is decoupled into two subproblems and solved iteratively by semidefinite programming and a manifold-based Riemannian steepest descent algorithm. We further design energy-aware beamforming to eliminate the interference induced by radar probing signals. Antenna selection with the norm is introduced to exclude redundant antennas while maintaining the sufficient multiple beams for multiple users and targets with minimized required antennas. Due to the nonconvexity of the -norm, we relax the number of active transmit antennas as a weighted -norm and employ a concave approximation for the constraint on the radar beampattern. Numerical results illustrate that the proposed algorithms can effectively reduce interference and strengthen the received signal power for radar sensing, achieving mutual benefit for communication and sensing performance.
{"title":"RIS-aided integrated sensing and communication: Beamforming design and antenna selection","authors":"Yuying Mai , Mateen Ashraf , Huiqin Du , Bo Tan","doi":"10.1016/j.sigpro.2024.109771","DOIUrl":"10.1016/j.sigpro.2024.109771","url":null,"abstract":"<div><div>This work considers an integrated sensing and communication system, where a reconfigurable intelligent surface (RIS) is utilized to manage interference and radar signals. The sensors are attached to the RIS to sense multiple targets. A joint design of the base station transmit beamforming and RIS phase shift matrix is proposed to minimize total interference and maximize the worst received signal power at the RIS sensors. Due to highly coupled transmit beamforming and RIS phase matrices, the optimization problem is decoupled into two subproblems and solved iteratively by semidefinite programming and a manifold-based Riemannian steepest descent algorithm. We further design energy-aware beamforming to eliminate the interference induced by radar probing signals. Antenna selection with the <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> norm is introduced to exclude redundant antennas while maintaining the sufficient multiple beams for multiple users and targets with minimized required antennas. Due to the nonconvexity of the <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>-norm, we relax the number of active transmit antennas as a weighted <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-norm and employ a concave approximation for the constraint on the radar beampattern. Numerical results illustrate that the proposed algorithms can effectively reduce interference and strengthen the received signal power for radar sensing, achieving mutual benefit for communication and sensing performance.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"229 ","pages":"Article 109771"},"PeriodicalIF":3.4,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706974","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}
Pub Date : 2024-11-07DOI: 10.1016/j.sigpro.2024.109768
Jin Ning, Jie Yin, Fei Deng, Lianbin Xie
Owing to the heterogeneous spatial distribution and non-uniform morphological characteristics of haze in remote sensing images (RSIs), conventional dehazing algorithms struggle to precisely recover the fine-grained details of terrestrial objects. To address this issue, a novel multi-scale attention boosted deformable Transformer (MABDT) tailored for RSI dehazing is proposed. This framework synergizes the multi-receptive field features elicited by convolutional neural network (CNN) with the long-term dependency features derived from Transformer, which facilitates a more adept restitution of texture and intricate detail information within RSIs. Firstly, spatial attention deformable convolution is introduced for computation of multi-head self-attention in the Transformer block, particularly in addressing complex haze scenarios encountered in RSIs. Subsequently, a multi-scale attention feature enhancement (MAFE) block is designed, tailored to capture local and multi-level detailed information features using multi-receptive field convolution operations, thereby accommodating non-uniform haze. Finally, a multi-level feature complementary fusion (MFCF) block is proposed, leveraging both shallow and deep features acquired from all encoding layers to augment each level of reconstructed image. The dehazing performance is evaluated on 6 open-source datasets, and quantitative and qualitative experimental results demonstrate the advancements of the proposed method in both metrical scores and visual quality. The source code is available at https://github.com/ningjin00/MABDT.
{"title":"MABDT: Multi-scale attention boosted deformable transformer for remote sensing image dehazing","authors":"Jin Ning, Jie Yin, Fei Deng, Lianbin Xie","doi":"10.1016/j.sigpro.2024.109768","DOIUrl":"10.1016/j.sigpro.2024.109768","url":null,"abstract":"<div><div>Owing to the heterogeneous spatial distribution and non-uniform morphological characteristics of haze in remote sensing images (RSIs), conventional dehazing algorithms struggle to precisely recover the fine-grained details of terrestrial objects. To address this issue, a novel multi-scale attention boosted deformable Transformer (MABDT) tailored for RSI dehazing is proposed. This framework synergizes the multi-receptive field features elicited by convolutional neural network (CNN) with the long-term dependency features derived from Transformer, which facilitates a more adept restitution of texture and intricate detail information within RSIs. Firstly, spatial attention deformable convolution is introduced for computation of multi-head self-attention in the Transformer block, particularly in addressing complex haze scenarios encountered in RSIs. Subsequently, a multi-scale attention feature enhancement (MAFE) block is designed, tailored to capture local and multi-level detailed information features using multi-receptive field convolution operations, thereby accommodating non-uniform haze. Finally, a multi-level feature complementary fusion (MFCF) block is proposed, leveraging both shallow and deep features acquired from all encoding layers to augment each level of reconstructed image. The dehazing performance is evaluated on 6 open-source datasets, and quantitative and qualitative experimental results demonstrate the advancements of the proposed method in both metrical scores and visual quality. The source code is available at <span><span>https://github.com/ningjin00/MABDT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"229 ","pages":"Article 109768"},"PeriodicalIF":3.4,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658958","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}
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