Pub Date : 2026-01-28DOI: 10.1016/j.sigpro.2026.110518
Shouharda Ghosh, Tarun Meena, Nithin V. George
Adaptive feedback cancellation (AFC) remains a significant challenge in digital hearing aids due to the correlation between the microphone input and loudspeaker output, leading to biased feedback path estimates. Additionally, loudspeaker-induced non-linearities, such as saturation, further degrade sound quality. This paper proposes an Enhanced Hammerstein-Spline Adaptive Filter (EHSAF) that improves upon the conventional Hammerstein-spline model by modifying the update rule to address convergence issues in sparse feedback paths. The integration of EHSAF within the AFC framework effectively mitigates non-linear distortions, ensuring improved stability and faster convergence. Further performance gains are achieved by incorporating the nearest Kronecker product (NKP) framework, which leverages the low-rank structure of the hearing aid impulse response. Experimental results demonstrate that the proposed EHSAF-based nonlinear AFC (NAFC) and NKP-enhanced EHSAF NAFC algorithms outperform state-of-the-art methods in both accuracy and computational efficiency.
{"title":"Low-rank enhanced Hammerstein-spline adaptive filter for sparsity-aware nonlinear feedback cancellation in hearing aids","authors":"Shouharda Ghosh, Tarun Meena, Nithin V. George","doi":"10.1016/j.sigpro.2026.110518","DOIUrl":"10.1016/j.sigpro.2026.110518","url":null,"abstract":"<div><div>Adaptive feedback cancellation (AFC) remains a significant challenge in digital hearing aids due to the correlation between the microphone input and loudspeaker output, leading to biased feedback path estimates. Additionally, loudspeaker-induced non-linearities, such as saturation, further degrade sound quality. This paper proposes an Enhanced Hammerstein-Spline Adaptive Filter (EHSAF) that improves upon the conventional Hammerstein-spline model by modifying the update rule to address convergence issues in sparse feedback paths. The integration of EHSAF within the AFC framework effectively mitigates non-linear distortions, ensuring improved stability and faster convergence. Further performance gains are achieved by incorporating the nearest Kronecker product (NKP) framework, which leverages the low-rank structure of the hearing aid impulse response. Experimental results demonstrate that the proposed EHSAF-based nonlinear AFC (NAFC) and NKP-enhanced EHSAF NAFC algorithms outperform state-of-the-art methods in both accuracy and computational efficiency.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"244 ","pages":"Article 110518"},"PeriodicalIF":3.6,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081759","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 : 2026-01-26DOI: 10.1016/j.sigpro.2026.110515
Yonghong Xiang, Le Yin, Yi Rong, Wenjing Xie
This paper introduces a regularized regression framework for robust state estimation of nonlinear systems. The nonlinear process and measurement functions are first approximated via statistical linearization, after which a Kalman-type estimator is derived through linear regression. The proposed framework generalizes several nonlinear Kalman filters and incorporates robust regularization to explicitly mitigate outlier effects. In particular, sparsity-promoting ℓ1-norm regularization enables joint estimation of outliers and state variables, thereby reducing the influence of error propagation across correlated components introduced by linearization. Furthermore, an ADMM-based algorithm is developed to naturally incorporate state constraints within the estimation framework. Numerical examples demonstrate that the proposed method achieves superior estimation accuracy compared to existing techniques.
{"title":"A regularized regression approach to robust state estimation of nonlinear systems with state constraints","authors":"Yonghong Xiang, Le Yin, Yi Rong, Wenjing Xie","doi":"10.1016/j.sigpro.2026.110515","DOIUrl":"10.1016/j.sigpro.2026.110515","url":null,"abstract":"<div><div>This paper introduces a regularized regression framework for robust state estimation of nonlinear systems. The nonlinear process and measurement functions are first approximated via statistical linearization, after which a Kalman-type estimator is derived through linear regression. The proposed framework generalizes several nonlinear Kalman filters and incorporates robust regularization to explicitly mitigate outlier effects. In particular, sparsity-promoting ℓ<sub>1</sub>-norm regularization enables joint estimation of outliers and state variables, thereby reducing the influence of error propagation across correlated components introduced by linearization. Furthermore, an ADMM-based algorithm is developed to naturally incorporate state constraints within the estimation framework. Numerical examples demonstrate that the proposed method achieves superior estimation accuracy compared to existing techniques.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"244 ","pages":"Article 110515"},"PeriodicalIF":3.6,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081760","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 : 2026-01-23DOI: 10.1016/j.sigpro.2026.110512
Shanli Chen , Dongyuan Lin , Peng Cai , Yunfei Zheng , Lei Zhang , Shiyuan Wang
Accurate estimation of latent states from noisy measurements remains a fundamental challenge in signal processing. Neural network-enhanced (NNE) Kalman filters, which integrate neural networks within traditional Kalman filtering frameworks, have emerged as a promising paradigm. However, in the presence of anomalous measurements, existing NNE Kalman filters often suffer from performance degradation. While certain approaches can alleviate this issue by adaptively adjusting the weights of anomalous measurements during the filtering process through end-to-end training, they are typically limited to specific types of anomalies, and their overall effectiveness remains constrained. To overcome these limitations, we propose an anomaly-robust NNE Kalman filter, called ARKFNet, that demonstrates superior performance across different kinds of anomaly scenarios. By integrating two dedicated neural network modules into the extended Kalman filter framework, ARKFNet replaces traditional anomaly-sensitive computations with a data-driven approach, establishing a unified framework for handling diverse anomaly types. To ensure stable training and numerical robustness, ARKFNet employs an alternating optimization strategy and enforces positive-definite constraints on its neural modules’ outputs through eigenvalue decomposition. Simulations demonstrate ARKFNet’s superior capability in addressing a range of anomalies, including false data injection attacks, sensor outliers, data mismatches, and missing data, outperforming existing NNE Kalman filters regarding estimation accuracy and robustness.
{"title":"ARKFNet: A neural network-enhanced anomaly-robust Kalman filter","authors":"Shanli Chen , Dongyuan Lin , Peng Cai , Yunfei Zheng , Lei Zhang , Shiyuan Wang","doi":"10.1016/j.sigpro.2026.110512","DOIUrl":"10.1016/j.sigpro.2026.110512","url":null,"abstract":"<div><div>Accurate estimation of latent states from noisy measurements remains a fundamental challenge in signal processing. Neural network-enhanced (NNE) Kalman filters, which integrate neural networks within traditional Kalman filtering frameworks, have emerged as a promising paradigm. However, in the presence of anomalous measurements, existing NNE Kalman filters often suffer from performance degradation. While certain approaches can alleviate this issue by adaptively adjusting the weights of anomalous measurements during the filtering process through end-to-end training, they are typically limited to specific types of anomalies, and their overall effectiveness remains constrained. To overcome these limitations, we propose an anomaly-robust NNE Kalman filter, called ARKFNet, that demonstrates superior performance across different kinds of anomaly scenarios. By integrating two dedicated neural network modules into the extended Kalman filter framework, ARKFNet replaces traditional anomaly-sensitive computations with a data-driven approach, establishing a unified framework for handling diverse anomaly types. To ensure stable training and numerical robustness, ARKFNet employs an alternating optimization strategy and enforces positive-definite constraints on its neural modules’ outputs through eigenvalue decomposition. Simulations demonstrate ARKFNet’s superior capability in addressing a range of anomalies, including false data injection attacks, sensor outliers, data mismatches, and missing data, outperforming existing NNE Kalman filters regarding estimation accuracy and robustness.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"244 ","pages":"Article 110512"},"PeriodicalIF":3.6,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049015","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 : 2026-01-22DOI: 10.1016/j.sigpro.2026.110504
Chen Li , Shuliang Wang , Yunzhe Men , Guoliang Chen
Abstract Two-dimensional (2-D) systems have been extensively investigated due to their effectiveness in modeling practical industrial processes. However, the presence of random mode switching and multiple disturbances may degrade the system performance or even induce instability. Driven by these challenges, this study focuses on a composite anti-disturbance asynchronous control strategy for 2-D semi-Markov jump Roesser systems subject to multiple disturbances. By fully considering the structural features of the Roesser model, a novel global mode generation mechanism is developed to address the issue of mode ambiguity. To counteract the detrimental influence of multiple disturbances, a 2-D disturbance observer is designed to compensate for matched disturbances arising from an exogenous system, while an energy-to-peak control scheme is employed to attenuate mismatched external disturbances. Since exact mode information is often unavailable in practical systems, a hidden Markov model is employed to handle the asynchrony in the controller-system channel. Sufficient conditions are derived to guarantee that the system is almost surely exponentially stable. Finally, the feasibility of the designed control methodology is validated through two simulation examples.
{"title":"Composite anti-disturbance asynchronous control for 2-D semi-Markov jump systems with multiple disturbances: From a mode generation perspective","authors":"Chen Li , Shuliang Wang , Yunzhe Men , Guoliang Chen","doi":"10.1016/j.sigpro.2026.110504","DOIUrl":"10.1016/j.sigpro.2026.110504","url":null,"abstract":"<div><div><strong>Abstract</strong> Two-dimensional (2-D) systems have been extensively investigated due to their effectiveness in modeling practical industrial processes. However, the presence of random mode switching and multiple disturbances may degrade the system performance or even induce instability. Driven by these challenges, this study focuses on a composite anti-disturbance asynchronous control strategy for 2-D semi-Markov jump Roesser systems subject to multiple disturbances. By fully considering the structural features of the Roesser model, a novel global mode generation mechanism is developed to address the issue of mode ambiguity. To counteract the detrimental influence of multiple disturbances, a 2-D disturbance observer is designed to compensate for matched disturbances arising from an exogenous system, while an energy-to-peak control scheme is employed to attenuate mismatched external disturbances. Since exact mode information is often unavailable in practical systems, a hidden Markov model is employed to handle the asynchrony in the controller-system channel. Sufficient conditions are derived to guarantee that the system is almost surely exponentially stable. Finally, the feasibility of the designed control methodology is validated through two simulation examples.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"244 ","pages":"Article 110504"},"PeriodicalIF":3.6,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081801","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 : 2026-01-20DOI: 10.1016/j.sigpro.2026.110503
Zhibo Tang, Heyue Huang, Xingpeng Mao
In passive radar systems, the utilized signals are typically not designed for radar purposes, resulting in high ambiguity floors. These ambiguity floors, compounded by strong direct-path and multipath clutter, often obscure weak targets. To enhance the signal-to-clutter ratio (SCR), clutter suppression algorithms are essential. The Extensive Cancellation Algorithm (ECA) and its variants are widely used for this purpose by projecting received signals onto the subspace orthogonal to clutter. However, ECA suffers from high computational cost as clutter space dimensionality increases. Segmented versions like ECA-Batches (ECA-B) and Generalized Subband Cancellation (GSC) reduce complexity by broadening the suppression notch in one domain on the range-Doppler (RD) map, but remain limited when addressing large-area clutter. In this paper, we propose ECA-Batches and Subbands (ECA-BS), which performs segmentation in both time and frequency domains. This dual-domain strategy simultaneously broadens the suppression notch in both delay and Doppler dimensions, significantly reducing the clutter space. Simulation experiments verify that ECA-BS achieves clutter suppression performance comparable to existing segmented methods while significantly reducing computational complexity. Its effectiveness is further confirmed by real-world data experiments, demonstrating strong practical applicability in large scale and complex clutter environments. These results make ECA-BS particularly well-suited for real-time passive radar applications.
{"title":"A low complexity method for large scale clutter suppression in passive radar","authors":"Zhibo Tang, Heyue Huang, Xingpeng Mao","doi":"10.1016/j.sigpro.2026.110503","DOIUrl":"10.1016/j.sigpro.2026.110503","url":null,"abstract":"<div><div>In passive radar systems, the utilized signals are typically not designed for radar purposes, resulting in high ambiguity floors. These ambiguity floors, compounded by strong direct-path and multipath clutter, often obscure weak targets. To enhance the signal-to-clutter ratio (SCR), clutter suppression algorithms are essential. The Extensive Cancellation Algorithm (ECA) and its variants are widely used for this purpose by projecting received signals onto the subspace orthogonal to clutter. However, ECA suffers from high computational cost as clutter space dimensionality increases. Segmented versions like ECA-Batches (ECA-B) and Generalized Subband Cancellation (GSC) reduce complexity by broadening the suppression notch in one domain on the range-Doppler (RD) map, but remain limited when addressing large-area clutter. In this paper, we propose ECA-Batches and Subbands (ECA-BS), which performs segmentation in both time and frequency domains. This dual-domain strategy simultaneously broadens the suppression notch in both delay and Doppler dimensions, significantly reducing the clutter space. Simulation experiments verify that ECA-BS achieves clutter suppression performance comparable to existing segmented methods while significantly reducing computational complexity. Its effectiveness is further confirmed by real-world data experiments, demonstrating strong practical applicability in large scale and complex clutter environments. These results make ECA-BS particularly well-suited for real-time passive radar applications.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"244 ","pages":"Article 110503"},"PeriodicalIF":3.6,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081761","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 : 2026-01-16DOI: 10.1016/j.sigpro.2026.110501
Ji Zhao , Xiaoyi Zhu , Qiang Li , Yi Yu , Guobing Qian , Hongbin Zhang
In non-Gaussian noise environments, the affine projection generalized maximum correntropy (APGMC) algorithm demonstrates strong robustness. To suppress error accumulation, this paper introduces the linear constraint strategy into APGMC and proposes a novel constrained affine projection generalized maximum correntropy (CAP-GMC) algorithm. Furthermore, to solve the problem of noisy input data, a constrained affine projection generalized maximum total correlation correntropy (CAP-GMTC) algorithm is proposed by combining the total least squares framework with the generalized Gaussian density function. For CAP-GMC and CAP-GMTC, we conduct the convergence analyses from the perspective of mean-square and mean senses to obtain their corresponding step-size bounds. In comparison with existing algorithms, several simulation results verify that the proposed CAP-GMC and CAP-GMTC achieve superior filtering performance in impulsive noise environments.
{"title":"A generalized maximum correntropy based constrained affine projection filtering algorithm and its total version","authors":"Ji Zhao , Xiaoyi Zhu , Qiang Li , Yi Yu , Guobing Qian , Hongbin Zhang","doi":"10.1016/j.sigpro.2026.110501","DOIUrl":"10.1016/j.sigpro.2026.110501","url":null,"abstract":"<div><div>In non-Gaussian noise environments, the affine projection generalized maximum correntropy (APGMC) algorithm demonstrates strong robustness. To suppress error accumulation, this paper introduces the linear constraint strategy into APGMC and proposes a novel constrained affine projection generalized maximum correntropy (CAP-GMC) algorithm. Furthermore, to solve the problem of noisy input data, a constrained affine projection generalized maximum total correlation correntropy (CAP-GMTC) algorithm is proposed by combining the total least squares framework with the generalized Gaussian density function. For CAP-GMC and CAP-GMTC, we conduct the convergence analyses from the perspective of mean-square and mean senses to obtain their corresponding step-size bounds. In comparison with existing algorithms, several simulation results verify that the proposed CAP-GMC and CAP-GMTC achieve superior filtering performance in impulsive noise environments.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"243 ","pages":"Article 110501"},"PeriodicalIF":3.6,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037936","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 : 2026-01-14DOI: 10.1016/j.sigpro.2026.110502
Yifei Wang , Kai-Li Yin , Xiaohong Yin , Chenggang Li , Lu Lu
Traditional Finite Impulse Response (FIR) structures suffer from high latency, making it difficult to operate efficiently at high frequencies. To address this issue, this paper presents a novel Non-Canonical FIR Maximum Correntropy Criterion (NCMCC) adaptive filtering algorithm. The non-canonical FIR structure optimizes the critical processing path latency by rearranging the delay units and reversing the weight coefficient sequence, thus enabling higher-frequency operation. By integrating the MCC algorithm, the proposed method enhances robustness against non-Gaussian and impulsive noise. A detailed theoretical analysis, including stochastic differential equation modeling and Lyapunov stability assessment, confirms the convergence and steady-state performance of the algorithm. Simulation results demonstrate that NCMCC outperforms conventional approaches such as Least Mean Square (LMS) algorithm, Least Mean p-th Power (LMP) algorithm and Sign Algorithm (SA) algorithm in terms of convergence speed, noise resilience, and steady-state error; Under various complex environments, the proposed algorithm demonstrates significantly improved performance compared to the MCC algorithm. These results establish NCMCC as an efficient and robust solution for real-time signal processing in complex and noisy environments.
{"title":"A low-latency FIR filter design based on maximum correntropy criterion: Design and performance evaluation","authors":"Yifei Wang , Kai-Li Yin , Xiaohong Yin , Chenggang Li , Lu Lu","doi":"10.1016/j.sigpro.2026.110502","DOIUrl":"10.1016/j.sigpro.2026.110502","url":null,"abstract":"<div><div>Traditional Finite Impulse Response (FIR) structures suffer from high latency, making it difficult to operate efficiently at high frequencies. To address this issue, this paper presents a novel Non-Canonical FIR Maximum Correntropy Criterion (NCMCC) adaptive filtering algorithm. The non-canonical FIR structure optimizes the critical processing path latency by rearranging the delay units and reversing the weight coefficient sequence, thus enabling higher-frequency operation. By integrating the MCC algorithm, the proposed method enhances robustness against non-Gaussian and impulsive noise. A detailed theoretical analysis, including stochastic differential equation modeling and Lyapunov stability assessment, confirms the convergence and steady-state performance of the algorithm. Simulation results demonstrate that NCMCC outperforms conventional approaches such as Least Mean Square (LMS) algorithm, Least Mean <em>p</em>-th Power (LMP) algorithm and Sign Algorithm (SA) algorithm in terms of convergence speed, noise resilience, and steady-state error; Under various complex environments, the proposed algorithm demonstrates significantly improved performance compared to the MCC algorithm. These results establish NCMCC as an efficient and robust solution for real-time signal processing in complex and noisy environments.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"243 ","pages":"Article 110502"},"PeriodicalIF":3.6,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037938","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 : 2026-01-13DOI: 10.1016/j.sigpro.2026.110499
Xiaoyu Qin, Bin Deng, Hongqiang Wang
Terahertz-Synthetic Aperture Radar (THz-SAR) offers high frame rates and high resolution, making it particularly suitable for remote sensing applications, like dynamic monitoring of moving targets. However, due to the non-ideal motion of the airborne platform and the non-cooperative motion of targets, this phenomenon causes more severe defocusing compared with microwave band SAR. Traditional SAR imaging methods, if directly applied to image THz-SAR moving targets, often suffer from poor quality and low efficiency. To address this issue, this article proposes a moving target non-parametric learning imaging method based on the Deep Unfolding Network (DUN) framework. Firstly, an autofocusing module is derived based on the maximum imaging contrast and embedded within the Alternating Direction Method of Multipliers (ADMM) iterative solution process to achieve accurate compensation of azimuthal motion errors. Then, we introduce the concept of Robust Principal Component Analysis (RPCA) to achieve sparse recovery imaging of moving targets. Finally, based on the ADMM iterative solution process, we establish an imaging network, named AF-RPCA-Net, efficiently achieving model-data jointly driven moving target background separation and imaging. The proposed method is validated to be effective and efficient through experimental results derived from both simulated and measured data.
{"title":"A high-resolution learning imaging method for THz-SAR moving targets based on AF-RPCA-Net","authors":"Xiaoyu Qin, Bin Deng, Hongqiang Wang","doi":"10.1016/j.sigpro.2026.110499","DOIUrl":"10.1016/j.sigpro.2026.110499","url":null,"abstract":"<div><div>Terahertz-Synthetic Aperture Radar (THz-SAR) offers high frame rates and high resolution, making it particularly suitable for remote sensing applications, like dynamic monitoring of moving targets. However, due to the non-ideal motion of the airborne platform and the non-cooperative motion of targets, this phenomenon causes more severe defocusing compared with microwave band SAR. Traditional SAR imaging methods, if directly applied to image THz-SAR moving targets, often suffer from poor quality and low efficiency. To address this issue, this article proposes a moving target non-parametric learning imaging method based on the Deep Unfolding Network (DUN) framework. Firstly, an autofocusing module is derived based on the maximum imaging contrast and embedded within the Alternating Direction Method of Multipliers (ADMM) iterative solution process to achieve accurate compensation of azimuthal motion errors. Then, we introduce the concept of Robust Principal Component Analysis (RPCA) to achieve sparse recovery imaging of moving targets. Finally, based on the ADMM iterative solution process, we establish an imaging network, named AF-RPCA-Net, efficiently achieving model-data jointly driven moving target background separation and imaging. The proposed method is validated to be effective and efficient through experimental results derived from both simulated and measured data.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"243 ","pages":"Article 110499"},"PeriodicalIF":3.6,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978305","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}
Preventing the radar transmitter from being utilized by an adversary as a non-cooperative bistatic illuminator is crucial for advanced surveillance systems. In this paper, a non-cooperative bistatic denial paradigm with coherent frequency diverse array (FDA) transmitter is proposed. The FDA achieves an angle-time/range-dependent beampattern by applying a slight frequency increment among array elements, resulting in transmitted signals that vary across different directions. This inherent anisotropic property decorrelates the target echo and direct-path signal received by the non-cooperative receiver. The signal processing output at the non-cooperative receiver is derived, demonstrating that the anisotropy of the coherent FDA transmitted signal degrades the target signal-to-noise ratio after pulse compression, thereby deteriorating the target detection capability of the non-cooperative receiver. Furthermore, the cross-correlation function (CCF) between the transmitted signals in the target and non-cooperative receiver directions is calculated, and two evaluation criteria, i.e., the peak loss and average loss of the CCF, are defined to quantitatively analyze the denial capability of the coherent FDA transmitter. The influence of FDA transmitter parameters and non-cooperative bistatic geometry on the denial performance is thoroughly investigated. Simulation results validate the effectiveness of the proposed method.
{"title":"Non-cooperative bistatic denial by using coherent FDA radar transmitter","authors":"Qingyun Kan , Jingwei Xu , Yuhong Zhang , Yanhong Xu , Guisheng Liao","doi":"10.1016/j.sigpro.2026.110500","DOIUrl":"10.1016/j.sigpro.2026.110500","url":null,"abstract":"<div><div>Preventing the radar transmitter from being utilized by an adversary as a non-cooperative bistatic illuminator is crucial for advanced surveillance systems. In this paper, a non-cooperative bistatic denial paradigm with coherent frequency diverse array (FDA) transmitter is proposed. The FDA achieves an angle-time/range-dependent beampattern by applying a slight frequency increment among array elements, resulting in transmitted signals that vary across different directions. This inherent anisotropic property decorrelates the target echo and direct-path signal received by the non-cooperative receiver. The signal processing output at the non-cooperative receiver is derived, demonstrating that the anisotropy of the coherent FDA transmitted signal degrades the target signal-to-noise ratio after pulse compression, thereby deteriorating the target detection capability of the non-cooperative receiver. Furthermore, the cross-correlation function (CCF) between the transmitted signals in the target and non-cooperative receiver directions is calculated, and two evaluation criteria, i.e., the peak loss and average loss of the CCF, are defined to quantitatively analyze the denial capability of the coherent FDA transmitter. The influence of FDA transmitter parameters and non-cooperative bistatic geometry on the denial performance is thoroughly investigated. Simulation results validate the effectiveness of the proposed method.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"243 ","pages":"Article 110500"},"PeriodicalIF":3.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037937","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 : 2026-01-09DOI: 10.1016/j.sigpro.2026.110489
Zerui Zhu , Dongmei Liu , Huaxiang Zhang , Li Liu , Fengfei Jin
RGB-T salient object detection (RGB-T SOD) aims to accurately localize salient objects by integrating complementary cues from RGB and thermal images, yet existing methods often overlook critical frequency-domain information. Our frequency-domain analysis reveals modality inconsistencies in salient regions, highlighting the need for adaptive modality evaluation. To address this issue, we propose a two-dimensional information entropy-based weighting strategy that quantifies structural complexity and adaptively guides modality contribution. Building upon this strategy, we develop the Dual-Domain Entropy-Aware Network (DEANet), which incorporates a Progressive Dual-domain Fusion and Refinement (PDFR) design-a coherent two-stage progressive mechanism. Stage 1 performs entropy-guided spatial-frequency interaction to generate high-quality fused features, while Stage 2 leverages these fused features to enhance original modality representations and refine saliency through spatial-channel perception. This progressive dual-domain formulation enables robust multimodal fusion and more accurate saliency estimation under diverse imaging conditions. Extensive experiments on three public benchmarks demonstrate that DEANet consistently surpasses 17 state-of-the-art methods across multiple evaluation metrics.
RGB- t显著目标检测（RGB- t SOD）旨在通过整合来自RGB和热图像的互补线索来准确定位显著目标，但现有方法往往忽略了关键的频域信息。我们的频域分析揭示了显著区域的模态不一致，强调了自适应模态评估的必要性。为了解决这一问题，我们提出了一种基于二维信息熵的加权策略，该策略量化了结构复杂性并自适应地指导了模态的贡献。在此策略的基础上，我们开发了双域熵感知网络（DEANet），它结合了渐进式双域融合和细化（PDFR）设计-一种连贯的两阶段渐进机制。阶段1执行熵引导的空间频率交互以生成高质量的融合特征，而阶段2利用这些融合特征增强原始模态表征并通过空间通道感知改善显著性。这种渐进式双域公式能够实现鲁棒的多模态融合和在不同成像条件下更准确的显著性估计。在三个公共基准上进行的广泛实验表明，DEANet在多个评估指标上始终超过17种最先进的方法。
{"title":"DEANet : Adaptive RGB-T salient object detection with two-dimensional entropy-guided dual-domain feature interaction","authors":"Zerui Zhu , Dongmei Liu , Huaxiang Zhang , Li Liu , Fengfei Jin","doi":"10.1016/j.sigpro.2026.110489","DOIUrl":"10.1016/j.sigpro.2026.110489","url":null,"abstract":"<div><div>RGB-T salient object detection (RGB-T SOD) aims to accurately localize salient objects by integrating complementary cues from RGB and thermal images, yet existing methods often overlook critical frequency-domain information. Our frequency-domain analysis reveals modality inconsistencies in salient regions, highlighting the need for adaptive modality evaluation. To address this issue, we propose a two-dimensional information entropy-based weighting strategy that quantifies structural complexity and adaptively guides modality contribution. Building upon this strategy, we develop the Dual-Domain Entropy-Aware Network (DEANet), which incorporates a Progressive Dual-domain Fusion and Refinement (PDFR) design-a coherent two-stage progressive mechanism. Stage 1 performs entropy-guided spatial-frequency interaction to generate high-quality fused features, while Stage 2 leverages these fused features to enhance original modality representations and refine saliency through spatial-channel perception. This progressive dual-domain formulation enables robust multimodal fusion and more accurate saliency estimation under diverse imaging conditions. Extensive experiments on three public benchmarks demonstrate that DEANet consistently surpasses 17 state-of-the-art methods across multiple evaluation metrics.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"243 ","pages":"Article 110489"},"PeriodicalIF":3.6,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978346","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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