Pub Date : 2025-11-08DOI: 10.1016/j.sigpro.2025.110400
Wei Zhang, Pei Zeng, Bo Ou
Video reversible data hiding (V-RDH) is widely applied in various fields to protect the security and integrity of data. In this paper, a new V-RDH method for high efficiency video coding (HEVC) is proposed by using histogram shifting (HS) and matrix embedding. Unlike previous HS-based algorithms that exhibit arbitrariness in selection for peak and zero bins, we propose a new strategy to trade off the capacity versus the distortion drift. The invertible matrix embedding is designed to improve the embedding efficiency. Our method does not need any side information for reversibility, and the distortion drift can be eliminated by the modifications without causing error propagation. Experimental results demonstrate that compared with the existing well-performing methods, the proposed method can achieve a better visual quality of the marked video with the satisfactory embedding capacity.
{"title":"Video reversible data hiding using histogram shifting and matrix embedding for HEVC","authors":"Wei Zhang, Pei Zeng, Bo Ou","doi":"10.1016/j.sigpro.2025.110400","DOIUrl":"10.1016/j.sigpro.2025.110400","url":null,"abstract":"<div><div>Video reversible data hiding (V-RDH) is widely applied in various fields to protect the security and integrity of data. In this paper, a new V-RDH method for high efficiency video coding (HEVC) is proposed by using histogram shifting (HS) and matrix embedding. Unlike previous HS-based algorithms that exhibit arbitrariness in selection for peak and zero bins, we propose a new strategy to trade off the capacity versus the distortion drift. The invertible matrix embedding is designed to improve the embedding efficiency. Our method does not need any side information for reversibility, and the distortion drift can be eliminated by the modifications without causing error propagation. Experimental results demonstrate that compared with the existing well-performing methods, the proposed method can achieve a better visual quality of the marked video with the satisfactory embedding capacity.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"241 ","pages":"Article 110400"},"PeriodicalIF":3.6,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528909","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 : 2025-11-07DOI: 10.1016/j.sigpro.2025.110388
Zixiang Zhou , Xiongjun Fu , Jian Dong , Meijing Gao , Ping Lang
Currently, multi-function radars (MFRs) are characterized by low probability interception, flexible beam control, and rapidly changing operation modes with pulse groups as basic units, which pose a severe challenge for electronic support (ES) systems to accurately analyzing the MFR pulse sequences. Developing a pulse group extracting and analyzing method that minimizes reliance on prior information and demonstrates high robustness to non-ideal conditions is crucial for further recognition of the MFR operation mode. This article proposes a divergence-based pulse group extracting and inter-pulse modulation parameter estimation of MFR pulse sequences method, i.e., DPEME. It can sequentially perform outlier removal and parameter reconstruction, pulse group extracting, and inter-pulse modulation parameter estimation of non-ideal MFR pulse sequences, which can mitigate the ”batch increase” and ”batch reduction” effects in unsupervised pulse group extracting. The simulation results of non-ideal pulse sequences with six modulation types show that DPEME can achieve superior and more robust unsupervised pulse group extracting performance compared to several existing state-of-the-art methods.
{"title":"Divergence-based pulse group extracting and inter-pulse modulation parameter estimation of multifunction radar pulse sequences","authors":"Zixiang Zhou , Xiongjun Fu , Jian Dong , Meijing Gao , Ping Lang","doi":"10.1016/j.sigpro.2025.110388","DOIUrl":"10.1016/j.sigpro.2025.110388","url":null,"abstract":"<div><div>Currently, multi-function radars (MFRs) are characterized by low probability interception, flexible beam control, and rapidly changing operation modes with pulse groups as basic units, which pose a severe challenge for electronic support (ES) systems to accurately analyzing the MFR pulse sequences. Developing a pulse group extracting and analyzing method that minimizes reliance on prior information and demonstrates high robustness to non-ideal conditions is crucial for further recognition of the MFR operation mode. This article proposes a divergence-based pulse group extracting and inter-pulse modulation parameter estimation of MFR pulse sequences method, i.e., DPEME. It can sequentially perform outlier removal and parameter reconstruction, pulse group extracting, and inter-pulse modulation parameter estimation of non-ideal MFR pulse sequences, which can mitigate the ”batch increase” and ”batch reduction” effects in unsupervised pulse group extracting. The simulation results of non-ideal pulse sequences with six modulation types show that DPEME can achieve superior and more robust unsupervised pulse group extracting performance compared to several existing state-of-the-art methods.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"241 ","pages":"Article 110388"},"PeriodicalIF":3.6,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528937","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 : 2025-11-07DOI: 10.1016/j.sigpro.2025.110390
Lei Zhou , Hongqing Liu , Lu Gan , Yi Zhou , Maciej Niedźwiecki , Trieu-Kien Truong
This work studies the sparse adaptive filter designs for audio signal recovery under impulsive disturbance. By exploiting the sparse representation of desired signal and compressibility of impulsive disturbance, a joint sparse least mean -norm (JSLMP) optimization, in which -norm () measures the data fidelity and -norm () enforces sparse solutions, is developed, termed as -JSLMP. The filter weights update is derived using gradient descent, and the Adam and variable step size (VSS) are integrated to accelerate convergence and avoid potential local minima. For the special case of , namely -JSLMP, its convergence condition and mean square deviation (MSD) analysis are derived. Finally, an application framework for processing corrupted audio signals is developed. Extensive experiments are conducted on both synthetic and real-measured impulsive noise data, comparing the proposed method with traditional algorithms as well as the deep learning-based GTCRN model. Results demonstrate that the proposed method yields superior perceptual quality and significantly lower memory consumption compared to GTCRN under impulsive disturbance.
{"title":"A novel sparse adaptive filter for suppressing impulsive disturbance in audio signals","authors":"Lei Zhou , Hongqing Liu , Lu Gan , Yi Zhou , Maciej Niedźwiecki , Trieu-Kien Truong","doi":"10.1016/j.sigpro.2025.110390","DOIUrl":"10.1016/j.sigpro.2025.110390","url":null,"abstract":"<div><div>This work studies the sparse adaptive filter designs for audio signal recovery under impulsive disturbance. By exploiting the sparse representation of desired signal and compressibility of impulsive disturbance, a joint sparse least mean <span><math><mi>p</mi></math></span>-norm (JSLMP) optimization, in which <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span>-norm (<span><math><mrow><mn>1</mn><mo>≤</mo><mi>p</mi><mo>≤</mo><mn>2</mn></mrow></math></span>) measures the data fidelity and <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>q</mi></mrow></msub></math></span>-norm (<span><math><mrow><mn>0</mn><mo>≤</mo><mi>q</mi><mo>≤</mo><mn>1</mn></mrow></math></span>) enforces sparse solutions, is developed, termed as <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>q</mi></mrow></msub></math></span>-JSLMP. The filter weights update is derived using gradient descent, and the <em>Adam</em> and variable step size (VSS) are integrated to accelerate convergence and avoid potential local minima. For the special case of <span><math><mrow><mi>q</mi><mo>=</mo><mn>1</mn></mrow></math></span>, namely <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-JSLMP, its convergence condition and mean square deviation (MSD) analysis are derived. Finally, an application framework for processing corrupted audio signals is developed. Extensive experiments are conducted on both synthetic and real-measured impulsive noise data, comparing the proposed method with traditional algorithms as well as the deep learning-based GTCRN model. Results demonstrate that the proposed method yields superior perceptual quality and significantly lower memory consumption compared to GTCRN under impulsive disturbance.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"241 ","pages":"Article 110390"},"PeriodicalIF":3.6,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528912","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 : 2025-11-07DOI: 10.1016/j.sigpro.2025.110387
Ehsan Lari , Reza Arablouei , Vinay Chakravarthi Gogineni , Stefan Werner
Federated learning (FL) leverages client–server communications to train global models on decentralized data. However, communication noise or errors can impair model accuracy. To address this challenge, we propose a novel FL algorithm that enhances robustness against communication noise while also reducing communication load. We derive the proposed algorithm by solving the weighted least-squares (WLS) regression problem, framed as a distributed convex optimization problem over a federated network with random client scheduling, via the alternating direction method of multipliers (ADMM). To counteract the detrimental effects of cumulative communication noise, we introduce a key modification by eliminating the dual variable and implementing a new local model update at each participating client. This subtle yet effective change results in using a single noisy global model update at each client instead of two, improving robustness against additive communication noise. Furthermore, we incorporate another modification enabling clients to continue local updates even when not selected by the server, leading to substantial performance improvements. Our theoretical analysis confirms the convergence of the proposed algorithm in both mean and mean-square senses, even when the server communicates with a random subset of clients over noisy links. Numerical results validate the effectiveness of our algorithm and corroborate theoretical findings.
{"title":"Noise-robust and resource-efficient ADMM-based federated learning for WLS regression","authors":"Ehsan Lari , Reza Arablouei , Vinay Chakravarthi Gogineni , Stefan Werner","doi":"10.1016/j.sigpro.2025.110387","DOIUrl":"10.1016/j.sigpro.2025.110387","url":null,"abstract":"<div><div>Federated learning (FL) leverages client–server communications to train global models on decentralized data. However, communication noise or errors can impair model accuracy. To address this challenge, we propose a novel FL algorithm that enhances robustness against communication noise while also reducing communication load. We derive the proposed algorithm by solving the weighted least-squares (WLS) regression problem, framed as a distributed convex optimization problem over a federated network with random client scheduling, via the alternating direction method of multipliers (ADMM). To counteract the detrimental effects of cumulative communication noise, we introduce a key modification by eliminating the dual variable and implementing a new local model update at each participating client. This subtle yet effective change results in using a single noisy global model update at each client instead of two, improving robustness against additive communication noise. Furthermore, we incorporate another modification enabling clients to continue local updates even when not selected by the server, leading to substantial performance improvements. Our theoretical analysis confirms the convergence of the proposed algorithm in both mean and mean-square senses, even when the server communicates with a random subset of clients over noisy links. Numerical results validate the effectiveness of our algorithm and corroborate theoretical findings.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"241 ","pages":"Article 110387"},"PeriodicalIF":3.6,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528913","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 : 2025-11-07DOI: 10.1016/j.sigpro.2025.110389
Emile Ghizzo, Julien Lesouple, Carl Milner
In the context of GNSS signal processing, the carrier-to-noise density ratio () is a powerful metric for evaluating GNSS performance, analyzing interference effects, and monitoring reception quality. Although the modeling of in the presence of interference has been extensively discussed in the literature, the specific impact of spoofing remains unexplored. In fact, due to the similar structure between authentic and spoofed signals, the latter directly interferes with the true signal and cannot be considered as an equivalent additional and independent noise source. This paper investigates the impact of spoofing on both true and estimated and proposes analytical expressions for their biases in the presence of spoofing. It reveals situations where the correlator output becomes non-ergodic, inducing divergence between the true and estimated values, as well as significant degradation. Additionally, there is a high dependence on the receiver architecture (estimation method) and spoofing geometry. Finally, beyond GNSS spoofing applications, this study highlights the effect of non-ergodicity on estimation and underscores the importance of studying estimation under non-ergodic conditions.
{"title":"Assessing spoofing impact on GNSS receivers: Carrier-to-noise density ratio (C/N0) estimation","authors":"Emile Ghizzo, Julien Lesouple, Carl Milner","doi":"10.1016/j.sigpro.2025.110389","DOIUrl":"10.1016/j.sigpro.2025.110389","url":null,"abstract":"<div><div>In the context of GNSS signal processing, the carrier-to-noise density ratio (<span><math><mrow><mi>C</mi><mo>/</mo><msub><mrow><mi>N</mi></mrow><mrow><mn>0</mn></mrow></msub></mrow></math></span>) is a powerful metric for evaluating GNSS performance, analyzing interference effects, and monitoring reception quality. Although the modeling of <span><math><mrow><mi>C</mi><mo>/</mo><msub><mrow><mi>N</mi></mrow><mrow><mn>0</mn></mrow></msub></mrow></math></span> in the presence of interference has been extensively discussed in the literature, the specific impact of spoofing remains unexplored. In fact, due to the similar structure between authentic and spoofed signals, the latter directly interferes with the true signal and cannot be considered as an equivalent additional and independent noise source. This paper investigates the impact of spoofing on both true and estimated <span><math><mrow><mi>C</mi><mo>/</mo><msub><mrow><mi>N</mi></mrow><mrow><mn>0</mn></mrow></msub></mrow></math></span> and proposes analytical expressions for their biases in the presence of spoofing. It reveals situations where the correlator output becomes non-ergodic, inducing divergence between the true and estimated values, as well as significant <span><math><mrow><mi>C</mi><mo>/</mo><msub><mrow><mi>N</mi></mrow><mrow><mn>0</mn></mrow></msub></mrow></math></span> degradation. Additionally, there is a high dependence on the receiver architecture (estimation method) and spoofing geometry. Finally, beyond GNSS spoofing applications, this study highlights the effect of non-ergodicity on estimation and underscores the importance of studying estimation under non-ergodic conditions.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"241 ","pages":"Article 110389"},"PeriodicalIF":3.6,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528938","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 : 2025-11-06DOI: 10.1016/j.sigpro.2025.110386
Qiyang Xiao , Yu Li , Xiaodong Zhai , Wenlong Jiang , Yong Jin , Ke Yuan , Wentao Shi
To address the challenges of low recognition accuracy and high computational overhead in noisy underwater environments, this paper proposes a novel noise-robust and lightweight underwater acoustic target recognition method based on Band-Specific Constant Q Transform (BSCQT) and Dynamic Context-Aware Masking (DCAM). First, BSCQT achieves effective noise suppression and feature extraction through multi-band adaptive weighting and feature concatenation. Then, by combining frequency-adaptive pooling granularity with traditional lightweight context-aware masking, a dynamic context-aware masking (DCAM) mechanism is constructed to implement adaptive attention on BSCQT features, improving recognition accuracy while maintaining low computational complexity. Furthermore, a Dynamic Context-Aware Masking Network (DCAMNet) is developed based on DCAM for hierarchical feature learning, integrating cascaded DCAM dense TDNN blocks for efficient information transmission. Finally, within the DCAMNet architecture, target recognition is accomplished through global pooling and fully connected classification layers. Extensive experimental results demonstrate that the proposed method achieves 99.23% recognition accuracy with only 0.55G Floating Point Operations (FLOPs) computational complexity, showing significant improvement in recognition efficiency compared to existing state-of-the-art methods and verifying the effectiveness of our approach.
{"title":"A novel noise-robust and lightweight underwater acoustic target recognition method based on BSCQT and DCAM","authors":"Qiyang Xiao , Yu Li , Xiaodong Zhai , Wenlong Jiang , Yong Jin , Ke Yuan , Wentao Shi","doi":"10.1016/j.sigpro.2025.110386","DOIUrl":"10.1016/j.sigpro.2025.110386","url":null,"abstract":"<div><div>To address the challenges of low recognition accuracy and high computational overhead in noisy underwater environments, this paper proposes a novel noise-robust and lightweight underwater acoustic target recognition method based on Band-Specific Constant Q Transform (BSCQT) and Dynamic Context-Aware Masking (DCAM). First, BSCQT achieves effective noise suppression and feature extraction through multi-band adaptive weighting and feature concatenation. Then, by combining frequency-adaptive pooling granularity with traditional lightweight context-aware masking, a dynamic context-aware masking (DCAM) mechanism is constructed to implement adaptive attention on BSCQT features, improving recognition accuracy while maintaining low computational complexity. Furthermore, a Dynamic Context-Aware Masking Network (DCAMNet) is developed based on DCAM for hierarchical feature learning, integrating cascaded DCAM dense TDNN blocks for efficient information transmission. Finally, within the DCAMNet architecture, target recognition is accomplished through global pooling and fully connected classification layers. Extensive experimental results demonstrate that the proposed method achieves 99.23% recognition accuracy with only 0.55G Floating Point Operations (FLOPs) computational complexity, showing significant improvement in recognition efficiency compared to existing state-of-the-art methods and verifying the effectiveness of our approach.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"241 ","pages":"Article 110386"},"PeriodicalIF":3.6,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528910","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 : 2025-11-05DOI: 10.1016/j.sigpro.2025.110379
Jinyi Yang , Lin Chen , Xue Jiang , Wei Liu
Dynamic time-domain weighting (DTW) hybrid precoding has been proposed to approximate the performance of fully digital (FD) precoding by maintaining the phase shifter (PS)-based structure, especially in wideband scenario with beam squint. The DTW framework does not depend on steering vectors, and hence is applicable to far- and near-field scenarios. However, existing DTW is originally designed for a single radio frequency (RF) chain. Directly extension to multi-chains involves matrix inversion, which leads to numerical instability. To solve these issues, we propose a frequency-domain DTW hybrid precoding. Different from the existing DTW solving the analog matrices in time domain, we solve the frequency component of these matrices instead, which avoids the ill-conditioned pseudo-inverse operation in fully-connected multi-chains. Furthermore, we approximate the optimal analog precoder by utilizing the user channels instead of the overall channel, which introduces additional dimension of users/RF chains and useful information to improve the performance. Considering the practical constraints on PS, we design the slow-switching analog precoders with a smaller time dimension by incorporating the Discrete Fourier Transform (DFT) matrix and reshaping the signal reconstruction equation. Extensive simulation results with different channel models, system parameters, and practical concerns demonstrate the superiority of the proposed method compared with conventional wideband precoding and existing DTW hybrid precoding in terms of spectrum efficiency, energy efficiency, and gain spectrum.
{"title":"Frequency-domain signal reconstruction for wideband dynamic time-domain weighting hybrid precoding","authors":"Jinyi Yang , Lin Chen , Xue Jiang , Wei Liu","doi":"10.1016/j.sigpro.2025.110379","DOIUrl":"10.1016/j.sigpro.2025.110379","url":null,"abstract":"<div><div>Dynamic time-domain weighting (DTW) hybrid precoding has been proposed to approximate the performance of fully digital (FD) precoding by maintaining the phase shifter (PS)-based structure, especially in wideband scenario with beam squint. The DTW framework does not depend on steering vectors, and hence is applicable to far- and near-field scenarios. However, existing DTW is originally designed for a single radio frequency (RF) chain. Directly extension to multi-chains involves matrix inversion, which leads to numerical instability. To solve these issues, we propose a frequency-domain DTW hybrid precoding. Different from the existing DTW solving the analog matrices in time domain, we solve the frequency component of these matrices instead, which avoids the ill-conditioned pseudo-inverse operation in fully-connected multi-chains. Furthermore, we approximate the optimal analog precoder by utilizing the user channels instead of the overall channel, which introduces additional dimension of users/RF chains and useful information to improve the performance. Considering the practical constraints on PS, we design the slow-switching analog precoders with a smaller time dimension by incorporating the Discrete Fourier Transform (DFT) matrix and reshaping the signal reconstruction equation. Extensive simulation results with different channel models, system parameters, and practical concerns demonstrate the superiority of the proposed method compared with conventional wideband precoding and existing DTW hybrid precoding in terms of spectrum efficiency, energy efficiency, and gain spectrum.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"241 ","pages":"Article 110379"},"PeriodicalIF":3.6,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528935","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 : 2025-11-05DOI: 10.1016/j.sigpro.2025.110384
Shuixin Li , Jiecheng Chen , Qingtang Jiang , Jian Lu
In nature, signals often appear in the form of the superposition of multiple non-stationary signals. The overlap of signal components in the time–frequency domain poses a significant challenge for signal analysis. One approach to addressing this problem is to introduce an additional chirprate parameter and use the chirplet transform (CT) to elevate the two-dimensional time–frequency representation to a three-dimensional time–frequency–chirprate representation. From a certain point of view, the CT of a signal can be regarded as a special windowed linear canonical transform of that signal, undergoing a shift and a modulation.
In this paper, we develop this idea to propose a novel windowed linear canonical transform (WLCT), which provides a new time–frequency–chirprate representation. We discuss four types of WLCTs. In addition, we use a special X-ray transform to further sharpen the time–frequency–chirprate representation. Furthermore, we derive the corresponding three-dimensional synchrosqueezed transform, demonstrating that the WLCTs have great potential for three-dimensional signal separation.
{"title":"Synchrosqueezed windowed linear canonical transform: A method for mode retrieval from multicomponent signals with crossing instantaneous frequencies","authors":"Shuixin Li , Jiecheng Chen , Qingtang Jiang , Jian Lu","doi":"10.1016/j.sigpro.2025.110384","DOIUrl":"10.1016/j.sigpro.2025.110384","url":null,"abstract":"<div><div>In nature, signals often appear in the form of the superposition of multiple non-stationary signals. The overlap of signal components in the time–frequency domain poses a significant challenge for signal analysis. One approach to addressing this problem is to introduce an additional chirprate parameter and use the chirplet transform (CT) to elevate the two-dimensional time–frequency representation to a three-dimensional time–frequency–chirprate representation. From a certain point of view, the CT of a signal can be regarded as a special windowed linear canonical transform of that signal, undergoing a shift and a modulation.</div><div>In this paper, we develop this idea to propose a novel windowed linear canonical transform (WLCT), which provides a new time–frequency–chirprate representation. We discuss four types of WLCTs. In addition, we use a special X-ray transform to further sharpen the time–frequency–chirprate representation. Furthermore, we derive the corresponding three-dimensional synchrosqueezed transform, demonstrating that the WLCTs have great potential for three-dimensional signal separation.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"241 ","pages":"Article 110384"},"PeriodicalIF":3.6,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145475629","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 : 2025-11-05DOI: 10.1016/j.sigpro.2025.110380
Tao Cui , Peng Dong , Zhongliang Jing , Kai Shen , Wujun Chen , Baitao Tang
To overcome the limitations of traditional distributed average fusion under measurement uncertainty, this paper proposes a distributed weighted average consensus algorithm. Based on the estimated states of each platform, the Wasserstein distance is utilized to adaptively determine real-time fusion weights. A matrix formulation of the weighted consensus iterations is developed within the alternating direction method of multipliers (ADMM) framework, followed by an analysis of the algorithm’s convergence properties. Simulation results demonstrate that the proposed algorithm significantly improves fusion accuracy in complex environments.
{"title":"Distributed weighted average consensus fusion based on ADMM under measurement uncertainty","authors":"Tao Cui , Peng Dong , Zhongliang Jing , Kai Shen , Wujun Chen , Baitao Tang","doi":"10.1016/j.sigpro.2025.110380","DOIUrl":"10.1016/j.sigpro.2025.110380","url":null,"abstract":"<div><div>To overcome the limitations of traditional distributed average fusion under measurement uncertainty, this paper proposes a distributed weighted average consensus algorithm. Based on the estimated states of each platform, the Wasserstein distance is utilized to adaptively determine real-time fusion weights. A matrix formulation of the weighted consensus iterations is developed within the alternating direction method of multipliers (ADMM) framework, followed by an analysis of the algorithm’s convergence properties. Simulation results demonstrate that the proposed algorithm significantly improves fusion accuracy in complex environments.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"241 ","pages":"Article 110380"},"PeriodicalIF":3.6,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528936","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 : 2025-11-05DOI: 10.1016/j.sigpro.2025.110383
Wenwu Gong , Zhejun Huang , Jiaxin Lu , Lili Yang
Missing traffic data caused by sensor failures poses a significant challenge to reliable traffic prediction and control. Existing spatiotemporal imputation methods struggle in extreme cases with over 80% missing data and complex non-random patterns. To address these limitations, we propose the spatiotemporal regularized Tucker APProach (TuckerAPP), which integrates tensor sparsity with spatiotemporal constraints to jointly capture long-term trends and short-term dynamics. TuckerAPP offers two key advantages: (i) adaptive rank determination via full-size Tucker decomposition with core tensor sparsity, and (ii) hierarchical spatiotemporal modeling through graph-regularized spatial factors that encode road network topology and Toeplitz-constrained temporal factors that capture periodic traffic patterns. We further develop a multi-block alternating proximal gradient algorithm with guaranteed convergence for large-scale tensors. Extensive experiments on urban traffic and network flow datasets demonstrate that TuckerAPP consistently outperforms six state-of-the-art baselines under extreme missing scenarios. These results confirm TuckerAPP’s robustness in preserving spatiotemporal consistency and highlight its superiority over existing tensor-based approaches.
{"title":"TuckerAPP: A novel spatiotemporal Tucker decomposition approach for traffic imputation","authors":"Wenwu Gong , Zhejun Huang , Jiaxin Lu , Lili Yang","doi":"10.1016/j.sigpro.2025.110383","DOIUrl":"10.1016/j.sigpro.2025.110383","url":null,"abstract":"<div><div>Missing traffic data caused by sensor failures poses a significant challenge to reliable traffic prediction and control. Existing spatiotemporal imputation methods struggle in extreme cases with over 80% missing data and complex non-random patterns. To address these limitations, we propose the spatiotemporal regularized Tucker APProach (TuckerAPP), which integrates tensor sparsity with spatiotemporal constraints to jointly capture long-term trends and short-term dynamics. TuckerAPP offers two key advantages: (i) adaptive rank determination via full-size Tucker decomposition with core tensor sparsity, and (ii) hierarchical spatiotemporal modeling through graph-regularized spatial factors that encode road network topology and Toeplitz-constrained temporal factors that capture periodic traffic patterns. We further develop a multi-block alternating proximal gradient algorithm with guaranteed convergence for large-scale tensors. Extensive experiments on urban traffic and network flow datasets demonstrate that TuckerAPP consistently outperforms six state-of-the-art baselines under extreme missing scenarios. These results confirm TuckerAPP’s robustness in preserving spatiotemporal consistency and highlight its superiority over existing tensor-based approaches.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"240 ","pages":"Article 110383"},"PeriodicalIF":3.6,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466744","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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