Pub Date : 2026-01-21DOI: 10.1109/LSP.2026.3656465
Li Cho;Shi-Wei Chang
The Papoulis–Gerchberg iterative reconstruction (PGIR) algorithm has been widely applied across diverse signal processing tasks, and reliable performance prediction during the system design stage is crucial for ensuring its effectiveness. However, predicting PGIR performance for arbitrary signal lengths and observation patterns has long been computationally intractable due to the combinatorial explosion of possible configurations. This letter addresses the problem by analyzing convergence conditions and modeling reconstruction error distributions in both noise-free and noisy scenarios. The derived closed-form probability laws enable accurate prediction for individual geometries, and the observed concentration of the operator's spectral radius with increasing signal length further allows performance characterization based only on loss and knowledge ratios. Tresulting probabilistic framework thus provides the first scalable tool for predicting PGIR performance, validated through case studies in multicarrier communication systems.
{"title":"On the Performance of Discrete Papoulis–Gerchberg Type Iterative Reconstruction","authors":"Li Cho;Shi-Wei Chang","doi":"10.1109/LSP.2026.3656465","DOIUrl":"https://doi.org/10.1109/LSP.2026.3656465","url":null,"abstract":"The Papoulis–Gerchberg iterative reconstruction (PGIR) algorithm has been widely applied across diverse signal processing tasks, and reliable performance prediction during the system design stage is crucial for ensuring its effectiveness. However, predicting PGIR performance for arbitrary signal lengths and observation patterns has long been computationally intractable due to the combinatorial explosion of possible configurations. This letter addresses the problem by analyzing convergence conditions and modeling reconstruction error distributions in both noise-free and noisy scenarios. The derived closed-form probability laws enable accurate prediction for individual geometries, and the observed concentration of the operator's spectral radius with increasing signal length further allows performance characterization based only on loss and knowledge ratios. Tresulting probabilistic framework thus provides the first scalable tool for predicting PGIR performance, validated through case studies in multicarrier communication systems.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"718-722"},"PeriodicalIF":3.9,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082161","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: 10.1109/LSP.2026.3656054
Minjian Chen;Liquan Shen;Qi Teng;Shiwei Wang;Feifeng Wang
Compressed point clouds are increasingly used in machine vision tasks, which rely on key semantic regions of the point cloud such as geometric details and structural boundaries. However, existing point cloud compression methods for machine vision lack explicit awareness of geometrically induced semantic boundaries, causing semantic ambiguity in certain boundary regions during compression, thereby degrading machine vision performance. To address this issue, we propose a Dual-domain Boundary Aware Point Cloud Geometry Compression (DBA-PCGC) method that explicitly preserves semantic geometric boundaries from complementary spatial and frequency perspectives, enabling beneficial for machine vision tasks. Specifically, a Structure Aware Transform Module (SATM) exploits Gram matrix traces on local graphs to capture structural variations and highlight high-variation boundary regions, while compactly encoding smooth areas. In parallel, a Frequency Aware Transform Module (FATM) applies Chebyshev high-pass filtering to enhance high-frequency components corresponding to semantic geometric boundaries and suppress redundant low-frequency content. Experimental results on point cloud machine vision tasks demonstrate that our method achieves superior performance compared with existing compression approaches.
{"title":"DBA-PCGC: Dual-Domain Boundary Aware for Task-Friendly Point Cloud Geometry Compression","authors":"Minjian Chen;Liquan Shen;Qi Teng;Shiwei Wang;Feifeng Wang","doi":"10.1109/LSP.2026.3656054","DOIUrl":"https://doi.org/10.1109/LSP.2026.3656054","url":null,"abstract":"Compressed point clouds are increasingly used in machine vision tasks, which rely on key semantic regions of the point cloud such as geometric details and structural boundaries. However, existing point cloud compression methods for machine vision lack explicit awareness of geometrically induced semantic boundaries, causing semantic ambiguity in certain boundary regions during compression, thereby degrading machine vision performance. To address this issue, we propose a Dual-domain Boundary Aware Point Cloud Geometry Compression (DBA-PCGC) method that explicitly preserves semantic geometric boundaries from complementary spatial and frequency perspectives, enabling beneficial for machine vision tasks. Specifically, a Structure Aware Transform Module (SATM) exploits Gram matrix traces on local graphs to capture structural variations and highlight high-variation boundary regions, while compactly encoding smooth areas. In parallel, a Frequency Aware Transform Module (FATM) applies Chebyshev high-pass filtering to enhance high-frequency components corresponding to semantic geometric boundaries and suppress redundant low-frequency content. Experimental results on point cloud machine vision tasks demonstrate that our method achieves superior performance compared with existing compression approaches.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"713-717"},"PeriodicalIF":3.9,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082109","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}
Conventional clustering algorithms, such as $k$-means and its variants, often assume that data are linearly separable and that all samples contribute equally to the clustering process. However, real-world data usually lies on nonlinear manifolds and contains noisy or ambiguous samples, making such assumptions unrealistic. To address these challenges, we incorporate a Sample-Weighting mechanism into the Kernel Clustering model, which is based on the strategy of coupling Prototype Distance with Local Manifold information together (PDLM-SWKC). Specifically, PDLM-SWKC performs clustering in kernel space to capture nonlinear structures, while adaptively assigning sample weights according to both their proximity to cluster centers and their local manifold connectivity; Besides, the learned sample weights in turn guide graph affinity matrix learning to generate better topological relation matrix, achieving tight coupling between sample-weighted kernel clustering and topological manifold learning. This dual-driven weighting mechanism enhances the robustness and structural consistency, effectively emphasizing reliable samples and suppressing outliers. Extensive experiments on eight benchmark datasets demonstrate that PDLM-SWKC achieves superior performance compared with state-of-the-art clustering methods. Moreover, convergence and visualization analyses confirm its stability, interpretability, and strong capability in modeling complex nonlinear data distributions.
{"title":"Prototype Distance and Local Manifold Guided Sample-Weighted Kernel Clustering","authors":"Chang Wu;Pengxin Xu;Zhaohu Liu;Luyun Wang;Yong Peng","doi":"10.1109/LSP.2026.3655339","DOIUrl":"https://doi.org/10.1109/LSP.2026.3655339","url":null,"abstract":"Conventional clustering algorithms, such as <inline-formula><tex-math>$k$</tex-math></inline-formula>-means and its variants, often assume that data are linearly separable and that all samples contribute equally to the clustering process. However, real-world data usually lies on nonlinear manifolds and contains noisy or ambiguous samples, making such assumptions unrealistic. To address these challenges, we incorporate a Sample-Weighting mechanism into the Kernel Clustering model, which is based on the strategy of coupling Prototype Distance with Local Manifold information together (PDLM-SWKC). Specifically, PDLM-SWKC performs clustering in kernel space to capture nonlinear structures, while adaptively assigning sample weights according to both their proximity to cluster centers and their local manifold connectivity; Besides, the learned sample weights in turn guide graph affinity matrix learning to generate better topological relation matrix, achieving tight coupling between sample-weighted kernel clustering and topological manifold learning. This dual-driven weighting mechanism enhances the robustness and structural consistency, effectively emphasizing reliable samples and suppressing outliers. Extensive experiments on eight benchmark datasets demonstrate that PDLM-SWKC achieves superior performance compared with state-of-the-art clustering methods. Moreover, convergence and visualization analyses confirm its stability, interpretability, and strong capability in modeling complex nonlinear data distributions.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"658-662"},"PeriodicalIF":3.9,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082086","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 : 2026-01-19DOI: 10.1109/LSP.2026.3655611
Shaoping Xu;Hanyang Hu;Wuyong Tao
Unsupervised methods like deep image prior (DIP) leverage network priors for denoising without labeled data but suffer from slow convergence and overfitting, while deep random projector (DRP) improves efficiency via fixed weights and a random seed yet remains limited by its fully random initialization. In this work, we propose deep fixed projector (DFP), an enhanced DRP-based framework featuring three synergistic improvements: (1) initializing the seed with the noisy image to align optimization with the clean image manifold, (2) using pre-trained clean-to-clean encoder-decoder weights to embed structural priors and accelerate convergence, and (3) introducing inter-inference consistency (IIC), a self-supervised regularization that enforces output stability under input perturbations to suppress noise and reduce overfitting. Experiments show DFP consistently surpasses DIP, DRP, and recent variants in PSNR, while achieving faster convergence and robust denoising quality.
{"title":"Deep Fixed Projector: Fast Projection Network for Image Denoising via Frozen Weights and Inter-Inference Consistency","authors":"Shaoping Xu;Hanyang Hu;Wuyong Tao","doi":"10.1109/LSP.2026.3655611","DOIUrl":"https://doi.org/10.1109/LSP.2026.3655611","url":null,"abstract":"Unsupervised methods like deep image prior (DIP) leverage network priors for denoising without labeled data but suffer from slow convergence and overfitting, while deep random projector (DRP) improves efficiency via fixed weights and a random seed yet remains limited by its fully random initialization. In this work, we propose deep fixed projector (DFP), an enhanced DRP-based framework featuring three synergistic improvements: (1) initializing the seed with the noisy image to align optimization with the clean image manifold, (2) using pre-trained clean-to-clean encoder-decoder weights to embed structural priors and accelerate convergence, and (3) introducing inter-inference consistency (IIC), a self-supervised regularization that enforces output stability under input perturbations to suppress noise and reduce overfitting. Experiments show DFP consistently surpasses DIP, DRP, and recent variants in PSNR, while achieving faster convergence and robust denoising quality.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"708-712"},"PeriodicalIF":3.9,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082042","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 : 2026-01-19DOI: 10.1109/LSP.2026.3655351
Eunkyun Lee;Jongwook Chae;Sooyoung Park;Jong Won Shin
Neural speech and audio codecs have demonstrated decent quality of the decoded audio at low bitrates. They consist of three parts, an encoder, a decoder, and a quantizer. Residual vector quantization (RVQ) or multi-stage vector quantization in which the residual signal from the previous stage is quantized in the next stage is employed in many neural speech codecs and has exhibited good performance while providing bitrate scalability. In this letter, we propose the redundancy-reduced residual vector quantization (R3VQ) which improves the RVQ by inserting a neural network called a refiner. The role of the refiner is to reduce the power of the residual signal to be quantized by enhancing the estimate of the original speech from the quantized signals in the previous stages. We also present a part-wise (PW) training scheme suitable for the training of the neural speech codec with the R3VQ. Experimental results showed that the proposed R3VQ trained with a PW training scheme outperformed the RVQ in both objective measures for speech quality and subjective MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) test.
{"title":"R3VQ: Redundancy-Reduced Residual Vector Quantization for Low-Bitrate Neural Speech Coding","authors":"Eunkyun Lee;Jongwook Chae;Sooyoung Park;Jong Won Shin","doi":"10.1109/LSP.2026.3655351","DOIUrl":"https://doi.org/10.1109/LSP.2026.3655351","url":null,"abstract":"Neural speech and audio codecs have demonstrated decent quality of the decoded audio at low bitrates. They consist of three parts, an encoder, a decoder, and a quantizer. Residual vector quantization (RVQ) or multi-stage vector quantization in which the residual signal from the previous stage is quantized in the next stage is employed in many neural speech codecs and has exhibited good performance while providing bitrate scalability. In this letter, we propose the redundancy-reduced residual vector quantization (R3VQ) which improves the RVQ by inserting a neural network called a refiner. The role of the refiner is to reduce the power of the residual signal to be quantized by enhancing the estimate of the original speech from the quantized signals in the previous stages. We also present a part-wise (PW) training scheme suitable for the training of the neural speech codec with the R3VQ. Experimental results showed that the proposed R3VQ trained with a PW training scheme outperformed the RVQ in both objective measures for speech quality and subjective MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) test.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"693-697"},"PeriodicalIF":3.9,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082126","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 : 2026-01-19DOI: 10.1109/LSP.2026.3655346
Rui Sun;Yifan Zhang;Xiaolu Yu;Yuwei Dai;Yaofei Wang
The rapid progress of deepfake technology, which primarily manipulates facial identity and image semantics, has made detection and defense critically important. Conventional global watermarking methods offer limited capacity for protecting key semantic content, as they typically rely on uniformly distributed watermarks across the entire image. This letter presents a method that weave watermarks as intrinsic components into the semantic content of images (facial regions) in the latent space. By aligning watermark embedding regions with facial content, we establish an inherent fragility mechanism wherein any deepfake manipulation that modifies facial semantics inevitably disrupts the watermark, enabling precise detection. Simultaneously, adversarial training of the extractor ensures robustness against conventional signal processing operations. A local entropy perception module dynamically adjusts embedding intensity based on regional texture complexity, maintaining high perceptual fidelity. Extensive experiments indicate that compared to advanced methods, the proposed approach maintains robustness against conventional benign operations while achieving reliable detection of deepfake forgeries, thereby enabling precise protection of image semantic content.
{"title":"Semantic-Aware and Semi-Fragile Diffusion Watermarking for Proactive Deepfake Detection","authors":"Rui Sun;Yifan Zhang;Xiaolu Yu;Yuwei Dai;Yaofei Wang","doi":"10.1109/LSP.2026.3655346","DOIUrl":"https://doi.org/10.1109/LSP.2026.3655346","url":null,"abstract":"The rapid progress of deepfake technology, which primarily manipulates facial identity and image semantics, has made detection and defense critically important. Conventional global watermarking methods offer limited capacity for protecting key semantic content, as they typically rely on uniformly distributed watermarks across the entire image. This letter presents a method that weave watermarks as intrinsic components into the semantic content of images (facial regions) in the latent space. By aligning watermark embedding regions with facial content, we establish an inherent fragility mechanism wherein any deepfake manipulation that modifies facial semantics inevitably disrupts the watermark, enabling precise detection. Simultaneously, adversarial training of the extractor ensures robustness against conventional signal processing operations. A local entropy perception module dynamically adjusts embedding intensity based on regional texture complexity, maintaining high perceptual fidelity. Extensive experiments indicate that compared to advanced methods, the proposed approach maintains robustness against conventional benign operations while achieving reliable detection of deepfake forgeries, thereby enabling precise protection of image semantic content.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"688-692"},"PeriodicalIF":3.9,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082204","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 : 2026-01-15DOI: 10.1109/LSP.2026.3654546
Roope Salmi;Vesa Välimäki
Sample rate conversion, a common task in audio signal processing, can be performed with high quality using the fast Fourier transform (FFT) on the whole audio file. Before returning to the time domain using the inverse FFT, the sample rate of the signal is changed by either truncating or zero-padding the frequency-domain buffer. This operation leaves a discontinuity in the spectrum, which causes time-domain ringing at that frequency. The ringing can be suppressed by tapering the highest frequency bins. This letter introduces the double Dolph-Chebyshev window, a frequency-domain tapering function with a configurable level of ringing outside its main lobe in the transform domain. In comparison to basic cosine tapering, the proposed method provides, for example, a 150-dB suppression 91% faster. This letter improves the accuracy of FFT-based sample rate conversion, making it a practical tool for signal processing.
{"title":"Suppression of Nyquist Ringing in FFT-Based Sample Rate Conversion","authors":"Roope Salmi;Vesa Välimäki","doi":"10.1109/LSP.2026.3654546","DOIUrl":"https://doi.org/10.1109/LSP.2026.3654546","url":null,"abstract":"Sample rate conversion, a common task in audio signal processing, can be performed with high quality using the fast Fourier transform (FFT) on the whole audio file. Before returning to the time domain using the inverse FFT, the sample rate of the signal is changed by either truncating or zero-padding the frequency-domain buffer. This operation leaves a discontinuity in the spectrum, which causes time-domain ringing at that frequency. The ringing can be suppressed by tapering the highest frequency bins. This letter introduces the double Dolph-Chebyshev window, a frequency-domain tapering function with a configurable level of ringing outside its main lobe in the transform domain. In comparison to basic cosine tapering, the proposed method provides, for example, a 150-dB suppression 91% faster. This letter improves the accuracy of FFT-based sample rate conversion, making it a practical tool for signal processing.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"683-687"},"PeriodicalIF":3.9,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11354502","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-15DOI: 10.1109/LSP.2026.3654532
Franz Weißer;Wolfgang Utschick
The mean square error (MSE)-optimal estimator is known to be the conditional mean estimator (CME). This letter introduces a parametric channel estimation technique based on Bayesian estimation. This technique uses the estimated channel parameters to parameterize the well-known LMMSE channel estimator. We first derive an asymptotic CME formulation that holds for a wide range of priors on the channel parameters. Based on this, we show that parametric Bayesian channel estimation is MSE-optimal for high signal-to-noise ratio (SNR) and/or long coherence intervals, i.e., many noisy observations provided within one coherence interval. Numerical simulations validate the derived formulations.
{"title":"On the Asymptotic MSE-Optimality of Parametric Bayesian Channel Estimation in mmWave Systems","authors":"Franz Weißer;Wolfgang Utschick","doi":"10.1109/LSP.2026.3654532","DOIUrl":"https://doi.org/10.1109/LSP.2026.3654532","url":null,"abstract":"The mean square error (MSE)-optimal estimator is known to be the conditional mean estimator (CME). This letter introduces a parametric channel estimation technique based on Bayesian estimation. This technique uses the estimated channel parameters to parameterize the well-known LMMSE channel estimator. We first derive an asymptotic CME formulation that holds for a wide range of priors on the channel parameters. Based on this, we show that parametric Bayesian channel estimation is MSE-optimal for high signal-to-noise ratio (SNR) and/or long coherence intervals, i.e., many noisy observations provided within one coherence interval. Numerical simulations validate the derived formulations.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"653-657"},"PeriodicalIF":3.9,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11354545","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-15DOI: 10.1109/LSP.2026.3654540
Shichao Zhong;Zhongjie Ma;Xiaolu Zeng;Renjie Liu;Xiaopeng Yang
Building layout sensing of through-the-wall radar (TWR) plays a vital role in fields such as counter-terrorism operations and post-disaster rescue. Existing layout sensing methods based on TWR typically focus solely on either corner information or wall surface features, neglecting the complementarity between the two, which leads to low sensing accuracy in complex environments. To address this issue, we propose a Corner-Wall Sensing Network (CWSNet), a building layout sensing network that fuses corner and wall surface information. First, deep convolutional networks are used to extract wall and corner features from TWR images. Then, these complementary structural features are fused to form an integrated representation. Finally, a transformer-based dynamic graph reasoning module (DGRM) captures their spatial relationships, enabling high-precision layout sensing. Both simulated and real-world experimental datasets demonstrate that CWSNet significantly outperforms existing methods across multiple evaluation metrics, achieving superior wall localization accuracy and layout connectivity, while also exhibiting strong robustness and generalization capabilities.
{"title":"CWSNet: A Building Layout Sensing Network With Corner and Wall Information Fusion From Through-the-Wall Radar","authors":"Shichao Zhong;Zhongjie Ma;Xiaolu Zeng;Renjie Liu;Xiaopeng Yang","doi":"10.1109/LSP.2026.3654540","DOIUrl":"https://doi.org/10.1109/LSP.2026.3654540","url":null,"abstract":"Building layout sensing of through-the-wall radar (TWR) plays a vital role in fields such as counter-terrorism operations and post-disaster rescue. Existing layout sensing methods based on TWR typically focus solely on either corner information or wall surface features, neglecting the complementarity between the two, which leads to low sensing accuracy in complex environments. To address this issue, we propose a Corner-Wall Sensing Network (CWSNet), a building layout sensing network that fuses corner and wall surface information. First, deep convolutional networks are used to extract wall and corner features from TWR images. Then, these complementary structural features are fused to form an integrated representation. Finally, a transformer-based dynamic graph reasoning module (DGRM) captures their spatial relationships, enabling high-precision layout sensing. Both simulated and real-world experimental datasets demonstrate that CWSNet significantly outperforms existing methods across multiple evaluation metrics, achieving superior wall localization accuracy and layout connectivity, while also exhibiting strong robustness and generalization capabilities.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"703-707"},"PeriodicalIF":3.9,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082177","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: 10.1109/LSP.2026.3653400
Zhifu Jiang;Jianxin Wu;Lei Zhang
High mobility of space-based radar (SBR) platforms risks target velocities falling below the minimum detectable velocity (MDV), rendering them undetectable in main-lobe clutter. Aiming at multi-target tracking (MTT) in space-based multiple-input multiple-output (MIMO) radar systems, this paper proposes a joint beam and dwell time allocation (JBTA) strategy. This strategy incorporates the MDV constraint and adopts the Bayesian Cramér-Rao Lower Bound (BCRLB) as the performance metric, where BCRLB is a lower bound for the mean square error (MSE) of target state estimation. To solve the non-convex mixed-integer optimization problem of JBTA, a two-step decomposition approach is designed. Numerical results verify that JBTA effectively improves global MTT performance.
{"title":"MTT Resource Allocation in Space-Based Netted MIMO Radar Under Main-Lobe Clutter","authors":"Zhifu Jiang;Jianxin Wu;Lei Zhang","doi":"10.1109/LSP.2026.3653400","DOIUrl":"https://doi.org/10.1109/LSP.2026.3653400","url":null,"abstract":"High mobility of space-based radar (SBR) platforms risks target velocities falling below the minimum detectable velocity (MDV), rendering them undetectable in main-lobe clutter. Aiming at multi-target tracking (MTT) in space-based multiple-input multiple-output (MIMO) radar systems, this paper proposes a joint beam and dwell time allocation (JBTA) strategy. This strategy incorporates the MDV constraint and adopts the Bayesian Cramér-Rao Lower Bound (BCRLB) as the performance metric, where BCRLB is a lower bound for the mean square error (MSE) of target state estimation. To solve the non-convex mixed-integer optimization problem of JBTA, a two-step decomposition approach is designed. Numerical results verify that JBTA effectively improves global MTT performance.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"624-628"},"PeriodicalIF":3.9,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082079","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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