In this paper, we propose a novel blind channel estimation framework for reconfigurable intelligent surface (RIS)-assisted millimeter wave (mmWave) wideband multi-user multiple-input single-output (MU-MISO) systems. Specifically, a joint cascaded and direct channel estimation approach is developed based on a structured subspace method. The proposed technique exploits the inherent Toeplitz structure of the channel matrix to formulate a cost function, which is subsequently minimized to recover accurate channel estimates. Unlike conventional methods, the proposed approach operates in a completely blind manner without relying on pilot signals, thereby conserving bandwidth while enhancing both spectral efficiency and overall system throughput. Simulation results are provided to illustrate the attractive benefits of the proposed method.
{"title":"Blind channel estimation for wideband RIS-assisted mmWave multi-user system with direct channels using structured subspace","authors":"Abdulmajid Lawal , Azzedine Zerguine , Karim Abed-Meraim","doi":"10.1016/j.sigpro.2025.110367","DOIUrl":"10.1016/j.sigpro.2025.110367","url":null,"abstract":"<div><div>In this paper, we propose a novel blind channel estimation framework for reconfigurable intelligent surface (RIS)-assisted millimeter wave (mmWave) wideband multi-user multiple-input single-output (MU-MISO) systems. Specifically, a joint cascaded and direct channel estimation approach is developed based on a structured subspace method. The proposed technique exploits the inherent Toeplitz structure of the channel matrix to formulate a cost function, which is subsequently minimized to recover accurate channel estimates. Unlike conventional methods, the proposed approach operates in a completely blind manner without relying on pilot signals, thereby conserving bandwidth while enhancing both spectral efficiency and overall system throughput. Simulation results are provided to illustrate the attractive benefits of the proposed method.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"240 ","pages":"Article 110367"},"PeriodicalIF":3.6,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466743","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 : 2025-10-30DOI: 10.1016/j.sigpro.2025.110377
Jielong Lu , Zhenkai Zhang , Boon-Chong Seet , Baiheng Wang
Dual-functional radar-communication (DFRC) has emerged as an effective solution in recent years to address spectrum scarcity in maritime environments, enabling efficient integrated communication and sensing. To mitigate path loss over complex sea surfaces, the intelligent reflecting surface (IRS) is introduced into DFRC systems, enhancing signal quality by providing an additional propagation path. To address the impact of sea wave fluctuations on the communication channel of maritime vessels, an alternating optimization (AO) algorithm based on semidefinite relaxation and fractional programming (SDR-FP) is proposed. First, the non-ideal channel state information (CSI) is modeled using a bounded channel uncertainty model via the S-procedure. Second, under constraints on radar detection performance and transmit power, the problem is formulated to maximize the communication sum-rate. Next, the proposed AO algorithm decomposes the original high-dimensional problem into two low-complexity subproblems. Finally, a minimization algorithm is applied to reformulate the non-convex subproblem into a tractable quadratically constrained quadratic program (QCQP). Simulation results demonstrate that the proposed method significantly enhances the communication sum-rate while achieving faster convergence compared to benchmarks.
{"title":"IRS-assisted communication performance optimization method for shipborne DFRC system","authors":"Jielong Lu , Zhenkai Zhang , Boon-Chong Seet , Baiheng Wang","doi":"10.1016/j.sigpro.2025.110377","DOIUrl":"10.1016/j.sigpro.2025.110377","url":null,"abstract":"<div><div>Dual-functional radar-communication (DFRC) has emerged as an effective solution in recent years to address spectrum scarcity in maritime environments, enabling efficient integrated communication and sensing. To mitigate path loss over complex sea surfaces, the intelligent reflecting surface (IRS) is introduced into DFRC systems, enhancing signal quality by providing an additional propagation path. To address the impact of sea wave fluctuations on the communication channel of maritime vessels, an alternating optimization (AO) algorithm based on semidefinite relaxation and fractional programming (SDR-FP) is proposed. First, the non-ideal channel state information (CSI) is modeled using a bounded channel uncertainty model via the S-procedure. Second, under constraints on radar detection performance and transmit power, the problem is formulated to maximize the communication sum-rate. Next, the proposed AO algorithm decomposes the original high-dimensional problem into two low-complexity subproblems. Finally, a minimization algorithm is applied to reformulate the non-convex subproblem into a tractable quadratically constrained quadratic program (QCQP). Simulation results demonstrate that the proposed method significantly enhances the communication sum-rate while achieving faster convergence compared to benchmarks.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"241 ","pages":"Article 110377"},"PeriodicalIF":3.6,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528911","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 : 2025-10-30DOI: 10.1016/j.sigpro.2025.110375
Weijian Liu , Hui Cao , Gaoqing Xiong , Jun Liu , Chongying Qi
The problem of detecting a point-like target in the presence of signal mismatch is considered in this paper. To design selective detectors, a random fictitious signal is introduced under the null hypothesis. This signal is designed to capture mismatched components through its specific structure, thereby enhancing the plausibility of the null hypothesis when signal mismatch occurs. The generalized likelihood ratio test (GLRT) criterion is adopted to solve the detection problem. Furthermore, a tunable detector is proposed based on the derived GLRT statistic to enable flexible enhancement of the selectivity. Closed-form expressions for the probabilities of detection (PDs) and false alarm (PFAs) are derived for both detectors, confirming their constant false alarm rate (CFAR) property. In the absence of signal mismatch, the proposed GLRT, with appropriate parameters, achieves a signal-to-noise ratio (SNR) gain of nearly 4 dB compared to the well-known whitened adaptive beamformer orthogonal rejection test (W-ABORT) at a PD of 0.9. When signal mismatch occurs, the proposed tunable GLRT exhibits superior selectivity against mismatched signals once the tuning parameter exceeds 0.4, outperforming the W-ABORT. The effectiveness of the proposed detectors has been validated through both simulations and real-data experiments.
{"title":"GLRT-based detectors with enhanced selectivity for mismatched signals through a random-signal approach","authors":"Weijian Liu , Hui Cao , Gaoqing Xiong , Jun Liu , Chongying Qi","doi":"10.1016/j.sigpro.2025.110375","DOIUrl":"10.1016/j.sigpro.2025.110375","url":null,"abstract":"<div><div>The problem of detecting a point-like target in the presence of signal mismatch is considered in this paper. To design selective detectors, a random fictitious signal is introduced under the null hypothesis. This signal is designed to capture mismatched components through its specific structure, thereby enhancing the plausibility of the null hypothesis when signal mismatch occurs. The generalized likelihood ratio test (GLRT) criterion is adopted to solve the detection problem. Furthermore, a tunable detector is proposed based on the derived GLRT statistic to enable flexible enhancement of the selectivity. Closed-form expressions for the probabilities of detection (PDs) and false alarm (PFAs) are derived for both detectors, confirming their constant false alarm rate (CFAR) property. In the absence of signal mismatch, the proposed GLRT, with appropriate parameters, achieves a signal-to-noise ratio (SNR) gain of nearly 4 dB compared to the well-known whitened adaptive beamformer orthogonal rejection test (W-ABORT) at a PD of 0.9. When signal mismatch occurs, the proposed tunable GLRT exhibits superior selectivity against mismatched signals once the tuning parameter exceeds 0.4, outperforming the W-ABORT. The effectiveness of the proposed detectors has been validated through both simulations and real-data experiments.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"240 ","pages":"Article 110375"},"PeriodicalIF":3.6,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466739","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 : 2025-10-29DOI: 10.1016/j.sigpro.2025.110364
Botao Jin , Pengwei Wen , Hui Yang , Yanqi Zhang , Zheng Yu
The performance of conventional constrained adaptive filtering algorithms tends to degrade in the presence of noisy input signals and impulsive background noise. To address this issue, we propose a novel algorithm called the constrained least total logical distance metric (CLTLDM), which is developed based on the error in variable model and the logical distance metric framework. Then, we derive analytical expressions for the algorithm’s step-size range, transient mean square deviation (MSD), and steady-state MSD to facilitate performance evaluation. To improve the algorithm’s ability to identify sparse systems, we further introduce an enhanced version of CLTLDM by incorporating an -norm constraint. Simulation results validate the proposed algorithm’s robustness and accuracy under various noise conditions and confirm the theoretical analysis.
{"title":"Constrained logical distance metric algorithm for noisy inputs and its sparse version","authors":"Botao Jin , Pengwei Wen , Hui Yang , Yanqi Zhang , Zheng Yu","doi":"10.1016/j.sigpro.2025.110364","DOIUrl":"10.1016/j.sigpro.2025.110364","url":null,"abstract":"<div><div>The performance of conventional constrained adaptive filtering algorithms tends to degrade in the presence of noisy input signals and impulsive background noise. To address this issue, we propose a novel algorithm called the constrained least total logical distance metric (CLTLDM), which is developed based on the error in variable model and the logical distance metric framework. Then, we derive analytical expressions for the algorithm’s step-size range, transient mean square deviation (MSD), and steady-state MSD to facilitate performance evaluation. To improve the algorithm’s ability to identify sparse systems, we further introduce an enhanced version of CLTLDM by incorporating an <span><math><msub><mrow><mi>l</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-norm constraint. Simulation results validate the proposed algorithm’s robustness and accuracy under various noise conditions and confirm the theoretical analysis.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"240 ","pages":"Article 110364"},"PeriodicalIF":3.6,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145419177","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 : 2025-10-29DOI: 10.1016/j.sigpro.2025.110368
Rammah Ibrahim, Qi Jiang, YunWei Zhao, Jie Wang
This paper presents the Adaptive Successive Variational Mode Decomposition (AS–VMD) method for denoising arterial pulse waves (APWs) and electrocardiograms (ECGs), effectively mitigating baseline wander, motion artifacts, and power-line interference without additional filtering. The approach performs adaptive baseline correction using the Discrete Wavelet Transform (DWT) with the Meyer wavelet, automatically selecting decomposition levels from the wavelet’s central frequency. VMD parameters () are estimated through a hybrid time–frequency strategy combining Short-Time Fourier Transform (STFT) power spectral density and Continuous Wavelet Transform (CWT) energy-peak detection. A two-stage decomposition refines intrinsic mode functions (IMFs) using kurtosis-based selection, CWT-guided sub-VMD, and percentile-based energy-correlation thresholds for reconstruction. Evaluations on the MIT-BIH Arrhythmia Database and self-measured APW data show SNRs of 12.10–23.12 dB (correlation = 0.816–0.981) for ECGs and 19.87–25.32 dB (correlation = 0.992–0.998) for APWs. An external test on photoplethysmography (PPG) signals from the BIDMC Database provides surrogate validation, confirming AS–VMD’s adaptability to related peripheral pulse waveforms. AS–VMD achieves an improved balance between noise suppression and waveform preservation compared with EMD, EEMD, CEEMDAN, and SSA, offering a filter-free, adaptive framework for clinical and wearable biomedical signal analysis.
{"title":"Adaptive successive variational mode decomposition for denoising ECG and arterial pulse waves","authors":"Rammah Ibrahim, Qi Jiang, YunWei Zhao, Jie Wang","doi":"10.1016/j.sigpro.2025.110368","DOIUrl":"10.1016/j.sigpro.2025.110368","url":null,"abstract":"<div><div>This paper presents the Adaptive Successive Variational Mode Decomposition (AS–VMD) method for denoising arterial pulse waves (APWs) and electrocardiograms (ECGs), effectively mitigating baseline wander, motion artifacts, and power-line interference without additional filtering. The approach performs adaptive baseline correction using the Discrete Wavelet Transform (DWT) with the Meyer wavelet, automatically selecting decomposition levels from the wavelet’s central frequency. VMD parameters (<span><math><mrow><mi>K</mi><mo>,</mo><mi>α</mi></mrow></math></span>) are estimated through a hybrid time–frequency strategy combining Short-Time Fourier Transform (STFT) power spectral density and Continuous Wavelet Transform (CWT) energy-peak detection. A two-stage decomposition refines intrinsic mode functions (IMFs) using kurtosis-based selection, CWT-guided sub-VMD, and percentile-based energy-correlation thresholds for reconstruction. Evaluations on the MIT-BIH Arrhythmia Database and self-measured APW data show SNRs of 12.10–23.12 dB (correlation = 0.816–0.981) for ECGs and 19.87–25.32 dB (correlation = 0.992–0.998) for APWs. An external test on photoplethysmography (PPG) signals from the BIDMC Database provides surrogate validation, confirming AS–VMD’s adaptability to related peripheral pulse waveforms. AS–VMD achieves an improved balance between noise suppression and waveform preservation compared with EMD, EEMD, CEEMDAN, and SSA, offering a filter-free, adaptive framework for clinical and wearable biomedical signal analysis.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"240 ","pages":"Article 110368"},"PeriodicalIF":3.6,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145419191","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 : 2025-10-28DOI: 10.1016/j.sigpro.2025.110366
Tingen Yu , Qi Feng , Jilei Liu , Luyan Ji , Xiurui Geng
The objective of infrared and visible image fusion technology is to generate fused images that retain rich detailed textures and prominent thermal radiation targets. However, current fusion approaches often suffer from insufficient interaction between features across different modalities and granularity levels, thereby compromising the quality of fused images. In this paper, we propose a hierarchical cross-modal and cross-granularity fusion network (HCCFNet) for infrared and visible image fusion tasks. Specifically, we introduce a novel deep-shallow cross-modal attention network that leverages the unique properties of multi-scale features. For effective and complementary fusion of the extracted cross-modal features, we strategically utilize a detail differential attention network for shallow features and a semantic cross-attention network for deep features. Furthermore, we design a semantic-detail cross-granularity feature optimization network to enable cross-granularity feature fusion guided progressively by deep-level features, preserving rich scene detail information while highlighting structural information. In addition, a comprehensive loss function is designed to generate fused images with salient targets and clear environmental details. The effectiveness of HCCFNet is validated through comprehensive ablation studies. Extensive qualitative and quantitative experiments conducted on four benchmark datasets demonstrate that HCCFNet outperforms 13 state-of-the-art methods.
{"title":"HCCFNet: Hierarchical cross-modal and cross-granularity fusion network for infrared and visible image fusion","authors":"Tingen Yu , Qi Feng , Jilei Liu , Luyan Ji , Xiurui Geng","doi":"10.1016/j.sigpro.2025.110366","DOIUrl":"10.1016/j.sigpro.2025.110366","url":null,"abstract":"<div><div>The objective of infrared and visible image fusion technology is to generate fused images that retain rich detailed textures and prominent thermal radiation targets. However, current fusion approaches often suffer from insufficient interaction between features across different modalities and granularity levels, thereby compromising the quality of fused images. In this paper, we propose a hierarchical cross-modal and cross-granularity fusion network (HCCFNet) for infrared and visible image fusion tasks. Specifically, we introduce a novel deep-shallow cross-modal attention network that leverages the unique properties of multi-scale features. For effective and complementary fusion of the extracted cross-modal features, we strategically utilize a detail differential attention network for shallow features and a semantic cross-attention network for deep features. Furthermore, we design a semantic-detail cross-granularity feature optimization network to enable cross-granularity feature fusion guided progressively by deep-level features, preserving rich scene detail information while highlighting structural information. In addition, a comprehensive loss function is designed to generate fused images with salient targets and clear environmental details. The effectiveness of HCCFNet is validated through comprehensive ablation studies. Extensive qualitative and quantitative experiments conducted on four benchmark datasets demonstrate that HCCFNet outperforms 13 state-of-the-art methods.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"240 ","pages":"Article 110366"},"PeriodicalIF":3.6,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145419175","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 : 2025-10-27DOI: 10.1016/j.sigpro.2025.110365
Wei Yu , Yunfei Zheng , Dongyuan Lin , Shiyuan Wang , Qiangqiang Zhang
To address the challenge of Bayesian filtering with unknown state transition model, we propose a Bayesian optimization framework using stochastic variational inference (BOSVI) for accurate modeling and state estimation. Specifically, the proposed BOSVI framework consists of a prior prediction network and a posterior correction network. First, the prior network models the latent system dynamics using a parameterized stochastic differential equation (SDE), allowing flexible approximation of nonlinear state evolution function. Then, an evidence lower bound (ELBO) is derived to optimize the SDE parameters and yield a prior distribution over the system states. Meanwhile, to correct deviations in the prior estimates, we design a neural network using real-time observations for posterior updates, which integrates GRU and self-attention mechanisms to dynamically refine state estimates and reduce uncertainty. Finally, simulation results demonstrate that, compared to other representative algorithms, the proposed BOSVI achieves superior estimation performance under various perturbation environments and observation mismatches.
{"title":"A Bayesian filtering network for state estimation with unknown system dynamics","authors":"Wei Yu , Yunfei Zheng , Dongyuan Lin , Shiyuan Wang , Qiangqiang Zhang","doi":"10.1016/j.sigpro.2025.110365","DOIUrl":"10.1016/j.sigpro.2025.110365","url":null,"abstract":"<div><div>To address the challenge of Bayesian filtering with unknown state transition model, we propose a Bayesian optimization framework using stochastic variational inference (BOSVI) for accurate modeling and state estimation. Specifically, the proposed BOSVI framework consists of a prior prediction network and a posterior correction network. First, the prior network models the latent system dynamics using a parameterized stochastic differential equation (SDE), allowing flexible approximation of nonlinear state evolution function. Then, an evidence lower bound (ELBO) is derived to optimize the SDE parameters and yield a prior distribution over the system states. Meanwhile, to correct deviations in the prior estimates, we design a neural network using real-time observations for posterior updates, which integrates GRU and self-attention mechanisms to dynamically refine state estimates and reduce uncertainty. Finally, simulation results demonstrate that, compared to other representative algorithms, the proposed BOSVI achieves superior estimation performance under various perturbation environments and observation mismatches.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"240 ","pages":"Article 110365"},"PeriodicalIF":3.6,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145419180","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}
Scene text image super-resolution (STISR) aims to enhance the visual quality of text images as well as improve the accuracy of downstream text recognition task. Although existing CNN-based super-resolution reconstruction models have made significant progress, these networks typically utilize smaller convolutional kernels to extract local image structures for text image representation, so they are not good at establishing long-range dependencies between text characters. To conquer this weakness, we propose a novel Transformer based on multi-head axial self-attention network for STISR. To be more specific, we perform self-attention calculations separately on the height axis, the width axis, and the channel axis to expand the receptive field and enhance the connections between pixels in the same horizontal and vertical directions. This allows the proposed method to generate high-quality text images without increasing extra computational complexity. Moreover, we formulate a multi-scale attention fusion module to strengthen the utilization of prior features by performing an attention-weighted fusion on both the generated high-level semantic priors and the shallow structure priors. Experimental results on the TextZoom benchmark dataset demonstrate that our proposed TextSRFormer significantly improves the recognition accuracy in the down-stream scene text recognition task while maintaining remarkably competitive quantitative quality assessment results. The code will be available at https://github.com/kbzhang0505/TextSRFormer.
{"title":"TextSRFormer: Multi-head axial self-attention transformer for scene text image super-resolution","authors":"Aobin Cheng , Xin He , Kaibing Zhang , Hui Zhang , Dinghua Xue","doi":"10.1016/j.sigpro.2025.110362","DOIUrl":"10.1016/j.sigpro.2025.110362","url":null,"abstract":"<div><div>Scene text image super-resolution (STISR) aims to enhance the visual quality of text images as well as improve the accuracy of downstream text recognition task. Although existing CNN-based super-resolution reconstruction models have made significant progress, these networks typically utilize smaller convolutional kernels to extract local image structures for text image representation, so they are not good at establishing long-range dependencies between text characters. To conquer this weakness, we propose a novel Transformer based on multi-head axial self-attention network for STISR. To be more specific, we perform self-attention calculations separately on the height axis, the width axis, and the channel axis to expand the receptive field and enhance the connections between pixels in the same horizontal and vertical directions. This allows the proposed method to generate high-quality text images without increasing extra computational complexity. Moreover, we formulate a multi-scale attention fusion module to strengthen the utilization of prior features by performing an attention-weighted fusion on both the generated high-level semantic priors and the shallow structure priors. Experimental results on the TextZoom benchmark dataset demonstrate that our proposed TextSRFormer significantly improves the recognition accuracy in the down-stream scene text recognition task while maintaining remarkably competitive quantitative quality assessment results. The code will be available at <span><span>https://github.com/kbzhang0505/TextSRFormer</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"240 ","pages":"Article 110362"},"PeriodicalIF":3.6,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145419176","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 : 2025-10-22DOI: 10.1016/j.sigpro.2025.110361
Mingjie Jia, Yan Sun, Wen-Qin Wang
Under far-field conditions, the clutter ridges of range-ambiguous clutter with different ambiguity factors are identical in the receive-Doppler plane for space–time adaptive processing (STAP). Based on the range-dependency of the transmit spatial frequency, frequency diverse array multiple-input multiple-output (FDA-MIMO) radar can separate the clutter ridges with different ambiguity number in the transmit-receive plane. However, scatter wave (SW) jamming is an effective electronic countermeasure (ECM) to deteriorate the performance of clutter suppression, especially the Doppler-modulated scatter wave (DMSW) jamming. In this paper, we investigate a parameter design strategy for FDA-MIMO radar under the presence of range-ambiguous clutter and SW or DMSW jamming. Through the analysis of the transmit-receive-Doppler phase relationship between the clutter and jamming signals, our proposed strategy can be applied to three range-ambiguous clutter scenarios, namely, without jamming, with SW jamming, and with DMSW jamming. Numerical results validate the effectiveness of the proposed strategy and demonstrates the advantages of FDA-MIMO radar over conventional arrayed radars against range-ambiguous clutter and SW-based jamming.
{"title":"FDA-MIMO radar parameter designing against range-ambiguous clutter and scatter-wave jamming","authors":"Mingjie Jia, Yan Sun, Wen-Qin Wang","doi":"10.1016/j.sigpro.2025.110361","DOIUrl":"10.1016/j.sigpro.2025.110361","url":null,"abstract":"<div><div>Under far-field conditions, the clutter ridges of range-ambiguous clutter with different ambiguity factors are identical in the receive-Doppler plane for space–time adaptive processing (STAP). Based on the range-dependency of the transmit spatial frequency, frequency diverse array multiple-input multiple-output (FDA-MIMO) radar can separate the clutter ridges with different ambiguity number in the transmit-receive plane. However, scatter wave (SW) jamming is an effective electronic countermeasure (ECM) to deteriorate the performance of clutter suppression, especially the Doppler-modulated scatter wave (DMSW) jamming. In this paper, we investigate a parameter design strategy for FDA-MIMO radar under the presence of range-ambiguous clutter and SW or DMSW jamming. Through the analysis of the transmit-receive-Doppler phase relationship between the clutter and jamming signals, our proposed strategy can be applied to three range-ambiguous clutter scenarios, namely, without jamming, with SW jamming, and with DMSW jamming. Numerical results validate the effectiveness of the proposed strategy and demonstrates the advantages of FDA-MIMO radar over conventional arrayed radars against range-ambiguous clutter and SW-based jamming.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"240 ","pages":"Article 110361"},"PeriodicalIF":3.6,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145419179","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 : 2025-10-22DOI: 10.1016/j.sigpro.2025.110345
Fethi Bencherki , Semiha Türkay , Hüseyin Akçay
In this paper, we propose a scheme to identify discrete-time, multi-input/multi-output switched-linear systems (MIMO-SLSs) from input–output measurements. The key step is an observer-based transformation to a switched auto-regressive with exogenous input (SARX) model. This transformation converts the state-space (SS) identification problem into a MIMO-SARX identification problem by compressing infinite strings of system Markov parameters into finite strings of observer Markov parameters. We study switch and discrete-state (submodel) identifiability and derive persistence of excitation conditions for hybrid inputs to recover discrete-states. Switching sequence and discrete-states are estimated in the observer domain by solving a convex-sparse optimization problem followed by two different subspace algorithms. Local-mode clustering then reveals discrete-states. A detailed numerical example illustrates performance of the proposed scheme.
{"title":"Multi-input/multi-output switched-linear system identification from input–output data","authors":"Fethi Bencherki , Semiha Türkay , Hüseyin Akçay","doi":"10.1016/j.sigpro.2025.110345","DOIUrl":"10.1016/j.sigpro.2025.110345","url":null,"abstract":"<div><div>In this paper, we propose a scheme to identify discrete-time, multi-input/multi-output switched-linear systems (MIMO-SLSs) from input–output measurements. The key step is an observer-based transformation to a switched auto-regressive with exogenous input (SARX) model. This transformation converts the state-space (SS) identification problem into a MIMO-SARX identification problem by compressing infinite strings of system Markov parameters into finite strings of observer Markov parameters. We study switch and discrete-state (submodel) identifiability and derive persistence of excitation conditions for hybrid inputs to recover discrete-states. Switching sequence and discrete-states are estimated in the observer domain by solving a convex-sparse optimization problem followed by two different subspace algorithms. Local-mode clustering then reveals discrete-states. A detailed numerical example illustrates performance of the proposed scheme.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"240 ","pages":"Article 110345"},"PeriodicalIF":3.6,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145365202","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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