Signal Processing最新文献

A low complexity method for large scale clutter suppression in passive radar 一种低复杂度的无源雷达大规模杂波抑制方法

IF 3.6 2区工程技术

Signal Processing Pub Date : 2026-07-01 Epub Date: 2026-01-20 DOI: 10.1016/j.sigpro.2026.110503

Zhibo Tang, Heyue Huang, Xingpeng Mao

{"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":"2026-07-01","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}

引用次数: 0

A regularized regression approach to robust state estimation of nonlinear systems with state constraints 带状态约束的非线性系统鲁棒状态估计的正则回归方法

IF 3.6 2区工程技术

Signal Processing Pub Date : 2026-07-01 Epub Date: 2026-01-26 DOI: 10.1016/j.sigpro.2026.110515

Yonghong Xiang, Le Yin, Yi Rong, Wenjing Xie

引用次数: 0

Low-rank enhanced Hammerstein-spline adaptive filter for sparsity-aware nonlinear feedback cancellation in hearing aids 用于助听器稀疏感知非线性反馈消除的低秩增强hammerstein样条自适应滤波器

IF 3.6 2区工程技术

Signal Processing Pub Date : 2026-07-01 Epub Date: 2026-01-28 DOI: 10.1016/j.sigpro.2026.110518

Shouharda Ghosh, Tarun Meena, Nithin V. George

引用次数: 0

Composite anti-disturbance asynchronous control for 2-D semi-Markov jump systems with multiple disturbances: From a mode generation perspective 多扰动二维半马尔可夫跳变系统的复合抗干扰异步控制：从模态生成的角度

IF 3.6 2区工程技术

Signal Processing Pub Date : 2026-07-01 Epub Date: 2026-01-22 DOI: 10.1016/j.sigpro.2026.110504

Chen Li , Shuliang Wang , Yunzhe Men , Guoliang Chen

{"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":"2026-07-01","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}

引用次数: 0

ARKFNet: A neural network-enhanced anomaly-robust Kalman filter 一种神经网络增强的异常鲁棒卡尔曼滤波器

IF 3.6 2区工程技术

Signal Processing Pub Date : 2026-07-01 Epub Date: 2026-01-23 DOI: 10.1016/j.sigpro.2026.110512

Shanli Chen , Dongyuan Lin , Peng Cai , Yunfei Zheng , Lei Zhang , Shiyuan Wang

{"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":"2026-07-01","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}

引用次数: 0

A noise-decoupled WLS solution for hybrid AOA-TDOA localization in the presence of sensor position errors 存在传感器位置误差的AOA-TDOA混合定位的噪声解耦WLS方法

IF 3.6 2区工程技术

Signal Processing Pub Date : 2026-06-01 Epub Date: 2026-01-05 DOI: 10.1016/j.sigpro.2025.110481

Yanbin Zou , Xiaofei Li , Shiru Chen , Yihan Wang , Yimao Sun

引用次数: 0

Possibility PMBM filter for robust multi-target tracking 可能性PMBM滤波器用于鲁棒多目标跟踪

IF 3.6 2区工程技术

Signal Processing Pub Date : 2026-06-01 Epub Date: 2026-01-01 DOI: 10.1016/j.sigpro.2025.110483

Lin Chen , Lin Gao , Yuxuan Xia , Chaoqun Yang , Zijie Shang , Zhicheng Su , Ting Yuan , Ping Wei

引用次数: 0

Non-fragile H∞ control of uncertain bilinear systems with signal quantization 带有信号量化的不确定双线性系统的非脆弱H∞控制

IF 3.6 2区工程技术

Signal Processing Pub Date : 2026-06-01 Epub Date: 2026-01-02 DOI: 10.1016/j.sigpro.2025.110463

Meng-Qi Wang, Xiao-Heng Chang

引用次数: 0

2-D DOA and polarization estimation using cylindrical coprime conformal array via cross-covariance tensor reconstruction 基于交叉协方差张量重构的柱质共形阵二维DOA和偏振估计

IF 3.6 2区工程技术

Signal Processing Pub Date : 2026-06-01 Epub Date: 2026-01-08 DOI: 10.1016/j.sigpro.2025.110485

Mingcheng Fu , Zhi Zheng , Ping Li , Wen-Qin Wang

引用次数: 0

MWNet: Image dehazing network based on multi-scale feature extraction and wavelet feature enhancement MWNet：基于多尺度特征提取和小波特征增强的图像去雾网络

IF 3.6 2区工程技术

Signal Processing Pub Date : 2026-06-01 Epub Date: 2026-01-07 DOI: 10.1016/j.sigpro.2026.110493

Haixin Jia , Han Wang , Yu Zhang , Guoying Zhang , Zhengfan Li , Hengchen Xu

{"title":"MWNet: Image dehazing network based on multi-scale feature extraction and wavelet feature enhancement","authors":"Haixin Jia , Han Wang , Yu Zhang , Guoying Zhang , Zhengfan Li , Hengchen Xu","doi":"10.1016/j.sigpro.2026.110493","DOIUrl":"10.1016/j.sigpro.2026.110493","url":null,"abstract":"<div><div>Atmospheric haze degrades image quality, impairing downstream vision tasks like object detection and segmentation. While wavelet-based deep learning methods are effective by leveraging lossless downsampling and spectral discrepancies, they often suffer from limited multi-scale feature extraction, inadequate frequency-domain enhancement, and a lack of structural priors. To overcome these issues, we propose MWNet, a novel framework integrating structural constraints into a U-Net with wavelet transforms. Our approach introduces dense multi-scale blocks for robust feature extraction, a hierarchical attention mechanism for high-frequency detail enhancement, and a cross-enhancement module for frequency feature interaction. Extensive experiments conducted on four benchmark datasets (SOTS-Indoor, Haze4K, Dense-Haze, NH-Haze) have demonstrated consistent superiority, with MWNet achieving SOTA in quantitative results compared to existing advanced methods (Surpassing the second-best method with average improvements of 0.16 dB in PSNR and 0.0026 in SSIM.), while qualitative results demonstrate enhanced detail preservation and noise suppression. In addition, we conducted generalization tests on three other datasets (RTTS, REAL-NH, CM-Haze), fully verifying the good generalization performance of MWNet.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"243 ","pages":"Article 110493"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978340","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}

引用次数: 0