Digital Signal Processing最新文献

{"title":"A framework of cooperative resource scheduling and beamforming in networked node system for multi-target tracking under distributed collaborative interferences","authors":"Yi Zhang,&nbsp;Haihong Tao,&nbsp;Jingjing Guo,&nbsp;Yingfei Yan","doi":"10.1016/j.dsp.2025.105604","DOIUrl":"10.1016/j.dsp.2025.105604","url":null,"abstract":"<div><div>Traditionally, networked node systems (NNSs) have typically focused on multi-target tracking (MTT) under ideal environments, ignoring the presence of malicious interferences that cause NNS malfunctions. Electronic countermeasures have historically relied on stationary and independently distributed jammers. However, the emergence of dynamic distributed collaborative interferences (DCIs) makes traditional anti-interference methods inadequate. Hence, in response to the dynamic nature of DCIs, we propose a framework of cooperative resource scheduling and beamforming (FCRSB) specifically tailored for optimal MTT performance under DCIs. This FCRSB includes the signal model of NNS, signal-level fusion (SLF), data-level fusion (DLF), and the resource scheduling. Firstly, we introduce a distributed adaptive beamforming algorithm and monopulse angle measurement for SLF in each cluster of the NNS. Subsequently, after acquiring the measurements, DLF between clusters is incorporated. Then, we derive the posterior Cramér-Rao lower bound (PCRLB) for MTT in this scenario, which serves as the objective function to formulate the resource optimization problem-an NP-hard problem. To address this challenge, we propose a combined decoupled relaxation constraint and sequential convex programming approach to solve it and obtain the optimal beam selection and corresponding transmit power within the tracking mode. Finally, through numerical simulation experiments, we demonstrate the effectiveness of the proposed FCRSB for MTT under two cases of DCIs.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105604"},"PeriodicalIF":3.0,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MSDCANet: A multi-scale dual-channel convolutional attention network for non-intrusive load disaggregation with enhanced feature extraction","authors":"Zeyi Geng ,&nbsp;Linfeng Yang ,&nbsp;Zhi Xie ,&nbsp;Yingzheng Li ,&nbsp;Zhiding Wu","doi":"10.1016/j.dsp.2025.105605","DOIUrl":"10.1016/j.dsp.2025.105605","url":null,"abstract":"<div><div>Non-Intrusive Load Monitoring (NILM) is a core smart grid technology that identifies the operating states of multiple appliances from a single access point, making it highly valuable in energy management and conservation. Although deep learning-based NILM methods have significantly improved feature extraction, the performance bottleneck has shifted beyond mere extraction accuracy as neural networks advance. Most models rely on single-scale features, overlooking multi-scale variations in power data caused by operational mode transitions. Appliance operation inherently exhibits multi-scale characteristics, with features at different scales jointly determining behavior; single-scale extraction may lead to overfitting and hinder differentiation of appliances with similar patterns. To address these limitations, we propose a Multi-Scale Dual-Channel Convolutional Attention Network (MSDCANet) for NILM tasks. MSDCANet integrates multi-scale feature extraction, adaptive normalization, and a multi-scale attention mechanism to extract and fuse features at various scales, enhancing disaggregation accuracy and model generalization. We evaluate it under origin-household and cross-household paradigms on the UK-DALE and REDD datasets. Experimental results demonstrate that MSDCANet outperforms state-of-the-art models in MAE, SAE, and F1 metrics for several high-energy-consuming appliances, confirming its applicability in complex usage scenarios and underscoring the importance of multi-scale techniques in NILM. The source code for our work is available at <span><span>https://github.com/linfengYang/MSDCANet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105605"},"PeriodicalIF":3.0,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low rank sparsity decomposition reverberation suppression combined with adaptive Kalman filtering method for detecting multi-ping moving target","authors":"Yubin Fu ,&nbsp;Xiaochuan Ma ,&nbsp;Yu Liu ,&nbsp;Xintong Wu ,&nbsp;Tianhang Ji ,&nbsp;Xingyuan Pei","doi":"10.1016/j.dsp.2025.105602","DOIUrl":"10.1016/j.dsp.2025.105602","url":null,"abstract":"<div><div>The traditional low rank sparsity decomposition (LRSD) can not separate the moving target from the sparse fluctuation reverberation and interference. Thus, the target is frequently masked by reverberation and interference. Therefore, the reverberation and interference suppression LRSD algorithm innovatively combined with the adaptive Kalman filtering and connected region (LRSD-CR-AMFKF) is proposed in this paper. The algorithm utilizes the connected region and adaptive Kalman filtering obtaining the continuous moving target trajectory and separates from the fluctuation reverberation and interference. Meanwhile, the adaptive Kalman filtering compensates for the measurement error caused by the interference, which improves the anti-interference ability of the algorithm and reduces the target estimation error. Finally, the LRSD-CR-AMFKF algorithm is validated by the experimental data. Compared with the conventional Kalman filtering and the LRSD, the sparse coefficient is improved, the reverberation and interference are separated, the estimation error is decreased, and a clean and precise target is obtained.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105602"},"PeriodicalIF":3.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A versatile interactive dual-path architecture with text modulation for multi-band image fusion","authors":"Jinmei Zhang ,&nbsp;Juntao Huang ,&nbsp;Jiangpeng Du ,&nbsp;Chao Zhang ,&nbsp;Mengdi Li","doi":"10.1016/j.dsp.2025.105569","DOIUrl":"10.1016/j.dsp.2025.105569","url":null,"abstract":"<div><div>Image fusion aims to integrate complementary information from source images to generate images with more comprehensive detail representation. Compared to conventional single-band images, multi-band images provide a wider array of radiative details and information. With the rapid advancement of deep learning techniques, the fusion of multi-band images can provide enhanced feature representations for target detection, demonstrating significant application value across military, medical, and environmental monitoring domains. Despite its growing popularity, image fusion remains a challenging problem due to inherent discrepancies in how different sources depict scene content. Current methods generally suffer from three limitations: 1) Sensitivity to slight misalignment between source images, leading to artifact generation in fused results; 2) Ineffective handling of interference caused by low quality source images, such as noise or degradation, etc. And 3) lack of interactive mechanisms to accommodate diverse subjective and objective requirements. To address these challenges, we propose a versatile interactive dual-path architecture with text modulation for multi-band image fusion. First, a micro registration deformation dual-path fusion module is designed, which employs explicit deformation fields to compensate for geometric misalignment, thereby mitigating artifacts, while incorporating a feature adaptive selection mechanism to enhance texture details and contrast. Second, we proposed a dynamic text modulated fusion module utilizing a dual-path attention mechanism, where text embeddings serve as conditional signals to drive both channel-wise and spatial attention weight generation, simultaneously addressing image quality degradation and interactive flexible fusion requirements. Extensive experiments conducted on two public benchmark datasets and one self-constructed multi-band infrared dataset prove that our method is superior to state-of-the-art (SOTA) methods in terms of quantitative evaluations and qualitative evaluations, effectively enhancing image fusion performance and degradation treatment.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105569"},"PeriodicalIF":3.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Matrix completion from quantized samples via generalized sparse Bayesian learning","authors":"Jiang Zhu ,&nbsp;Zhennan Liu ,&nbsp;Qi Zhang ,&nbsp;Yifan Wang","doi":"10.1016/j.dsp.2025.105575","DOIUrl":"10.1016/j.dsp.2025.105575","url":null,"abstract":"<div><div>The recovery of a low rank matrix from a subset of noisy low-precision quantized samples arises in various applications, such as collaborative filtering, intelligent recommendation and millimeter wave channel estimation with few bit analog-to-digital converters (ADCs). In this paper, a generalized sparse Bayesian learning algorithm (Gr-SBL) combined with expectation propagation (EP) is proposed to solve the matrix completion (MC), termed MC-Gr-SBL. The MC-Gr-SBL automatically estimates the rank, the factors and their covariance matrices, and the noise variance. In addition, MC-Gr-SBL is proposed to solve the two dimensional line spectral estimation problem by incorporating the MUSIC algorithm. Finally, numerical simulations and real data experiments are conducted to verify the effectiveness of the proposed algorithm.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105575"},"PeriodicalIF":3.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-UAV cooperative bearing-only target localization based on multi-level box particle filter","authors":"Qiyuan Yin ,&nbsp;Cheng Xu ,&nbsp;Peng Zhou ,&nbsp;Daqing Huang ,&nbsp;Wenxiao Xu","doi":"10.1016/j.dsp.2025.105572","DOIUrl":"10.1016/j.dsp.2025.105572","url":null,"abstract":"<div><div>This study addresses the challenge of enhancing the precision of maneuvering target localization using dual unmanned aerial vehicles (UAVs) equipped with angle-of-arrival (AOA) sensors. Traditional methods, operating in single-UAV mode, suffer from insufficient measurement dimensions, low measurement efficiency, and the highly nonlinear nature of angular measurement information. These factors impose stringent requirements on filter parameters, resulting in poor localization stability, complex parameter tuning, and significant limitations in practical applications. To tackle these issues, we propose a dual-UAV cooperative localization model based on box particle filtering. First, by reducing the dimensionality and unifying the original nonlinear measurement boxes, the computational efficiency of complex stochastic processes (CSP) is significantly improved. Second, a multi-level (ML) measurement box mechanism is designed, and through rigorous derivation, a method for calculating the weights of multi-level measurement boxes is defined. This mechanism not only effectively mitigates particle degradation during the filtering process but also further enhances the accuracy of measurement information. Finally, based on the multi-level box particle filtering model, we introduce an adaptive interval expansion (AIE) and adaptive adjustment method for maneuvering innovation. This approach leverages information generated by box particles to dynamically adjust the motion model parameters of maneuvering targets in real time, enabling the system to flexibly adapt to the high-mobility variations of adversarial targets. Extensive experimental results demonstrate that our model overcomes many shortcomings of traditional methods, providing an effective new approach for dual-UAV cooperative bearing-only target localization.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105572"},"PeriodicalIF":3.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CFPNet: Multivariate time series classification based on frequency domain reconstruction","authors":"Aiguo Li,&nbsp;Bowen Li","doi":"10.1016/j.dsp.2025.105598","DOIUrl":"10.1016/j.dsp.2025.105598","url":null,"abstract":"<div><div>Transformer-based deep learning models have significantly advanced the field of multivariate time series classification. However, the intrinsic self-attention mechanism renders existing Transformer methods prone to frequency bias and inadequate in extracting local features, which ultimately limits their representational capacity. To address these issues, we propose CFPNet, a novel network that enhances representation learning by reconstructing a Crucial Frequency Patch in the frequency domain, thereby effectively mitigating frequency bias. Additionally, we introduce the Wav-KAN encoder, which integrates wavelet transforms with the Kolmogorov-Arnold Network to accurately capture local dependencies. Extensive experiments on fourteen public datasets from the UEA(Multivariate Time Series Classification Archive), as well as on a custom dataset of ultrasonic signals from metallic materials, demonstrate that CFPNet achieves superior classification accuracy compared to state-of-the-art methods.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105598"},"PeriodicalIF":3.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised DL-based wireless signal anomaly detection","authors":"Xiangli Liu,&nbsp;Wei Tan,&nbsp;Zan Li,&nbsp;Junjie Zeng","doi":"10.1016/j.dsp.2025.105578","DOIUrl":"10.1016/j.dsp.2025.105578","url":null,"abstract":"<div><div>Detecting wireless signal anomalies in non-ideal channels and complex electromagnetic environments is a particularly challenging and demanding task. Susceptible to environmental influences, wireless signal anomalies are diverse, making its anomaly detection very difficult. To improve the detection of anomalous signals in complex electromagnetic environments, a novel unsupervised model, CAAEDS (Combined Adversarial Autoencoder and Deep SVDD) is proposed, which incorporates dual input modalities: time-domain data and Power Spectral Density (PSD). CAAEDS extracts time domain data feature information using Long Short-term Memory (LSTM) and PSD data feature information using Residual Networks (ResNets). Results from experiments demonstrate that: 1) The performance of the proposed algorithm outperforms state-of-the-art algorithms. 2) Ablation studies prove that CAAEDS can overcome the shortcomings of unsupervised AAE and Deep SVDD in wireless signal anomaly detection. 3) Wireless signal datasets collected in real-world environments verify the ability of CAAEDS to adapt to the environment and detect weak anomalies in wireless signals.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105578"},"PeriodicalIF":3.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Moving forward in water distribution network leak identification through an innovative features engineering step","authors":"Elvio Damonti,&nbsp;Giancarlo Bernasconi","doi":"10.1016/j.dsp.2025.105603","DOIUrl":"10.1016/j.dsp.2025.105603","url":null,"abstract":"<div><div>In Italy an average of 40% of the water in transit in Water Distribution Networks (WDNs) is lost, which represents a significant economic loss, and, because of the corresponding energy waste, also a considerable environmental damage. Over the years various methods have been developed for detecting and locating leaks in WDNs, and, in particular, several algorithms have been implemented that use Convolutional Neural Networks (CNNs). They are all based on a training phase run on a representative subset of the WDN data, and the main differences between the various implementations are in the data pre-processing and in the CNN configuration. This paper proposes a new fully Data-Driven approach, where a preliminar features engineering step, performed by a visual analysis of specific data patterns, both in the time domain and in the Fourier domain, allowed us to conceive and identify two paramount features engineering steps: a new effective data pre-processing algorythm and a new configuration of CNN that uses an Overcomplete Autoencoder (Overcomplete AE) topology with residual blocks. These two steps, described in detail in this paper, allowed us to better highlight and identify the anomalies caused by leaks in WDN pressures time series and they permitted, in association with a new original automatic analysis of the reconstruction error made by the Autoencoder, to achieve results that are on the top of the current state of art. Specifically, the whole innovative method is presented in detail exploiting publicly available data, so to be easily reproducible, and, more specifically, for this purpose, the benchmark was run on a synthetic 'LeakDB' dataset of 500 scenarios and the outcomes were then validated through different data obtained from a second and more complex synthetic 'LeakDB' dataset containing 1000 scenarios. Both these datasets are related to a District Metering Area (DMA) of the Hanoi WDN and both are publicly available.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105603"},"PeriodicalIF":3.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TunnelDiff: A brightness-guided image restoration diffusion model for enhancing defect detection in low-quality tunnel lining images","authors":"Xuexin Liang ,&nbsp;Lu Lu ,&nbsp;Qingyan Tian ,&nbsp;Haishan Lin ,&nbsp;Quanyi Zou","doi":"10.1016/j.dsp.2025.105581","DOIUrl":"10.1016/j.dsp.2025.105581","url":null,"abstract":"<div><div>During capturing in the tunnel, factors such as lining materials, illumination conditions, and imaging equipment may affect the quality of images and introduce noise. The low-quality images bring challenges in tunnel lining defect detection. This paper introduces TunnelDiff, a diffusion model designed to enhance tunnel images and perform better on defect detection. TunnelDiff restores image details by leveraging the inherent generalization ability of the pretrained Stable Diffusion Model. It also introduces the Condition Module to guide the generation direction. The Condition Module includes the Illumination Distribution Module (IDM) and the Brightness Guided Module (BGM). The IDM focuses on correcting uneven illumination in tunnel images, while the BGM addresses target brightness ambiguity in low-light correction tasks. Due to the absence of paired data in tunnel enhancement, TunnelDiff first trained on the Exposure Errors dataset and then enhanced the Tunnel Defect dataset. Experimental outcomes demonstrated improvements in image quality metrics on both datasets, and the Tunnel Defect dataset enhanced by TunnelDiff performed better in defect detection than datasets enhanced by other models. In particular, TunnelDiff showed better crack defect detection, with 2.03 %, 1.42 %, and 1.55 % improvement in crack recall, F1-score, and IoU. Additionally, TunnelDiff consistently produced images within a specific brightness range. These results underscore the effectiveness of TunnelDiff. The corresponding code is available at: <span><span>https://github.com/derolol/tunnel_diff.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105581"},"PeriodicalIF":3.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}