Digital Signal Processing最新文献

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An ISRJ suppression method based on time-frequency analysis combined with frequency-dimension projection 基于时频分析与频率维投影相结合的ISRJ抑制方法
IF 3 3区 工程技术
Digital Signal Processing Pub Date : 2025-09-06 DOI: 10.1016/j.dsp.2025.105599
Jun Luo , Hui Chen , Weijian Liu , Binbin Li , Pei Tian , Xiaoge Wang
{"title":"An ISRJ suppression method based on time-frequency analysis combined with frequency-dimension projection","authors":"Jun Luo ,&nbsp;Hui Chen ,&nbsp;Weijian Liu ,&nbsp;Binbin Li ,&nbsp;Pei Tian ,&nbsp;Xiaoge Wang","doi":"10.1016/j.dsp.2025.105599","DOIUrl":"10.1016/j.dsp.2025.105599","url":null,"abstract":"<div><div>To effectively suppress interrupted sampling repeater jamming (ISRJ), this paper proposes an ISRJ suppression method based on time-frequency analysis combined with frequency-dimension projection, leveraging the distinct time-frequency amplitude responses of target echo signals and ISRJ signals after dechirping. Firstly, a bidirectional sliding window detection method is used to locate the echo pulses. Subsequently, the time-frequency matrix of the localized region is projected onto the frequency dimension, and a differential method is applied to preliminarily identify peak points of jamming and targets. Then, the standard deviation from statistics is utilized as a discriminant metric to distinguish between target and jamming peaks, followed by the construction of a time-frequency filter for jamming suppression. Finally, performing inverse Short-Time Fourier Transform (STFT) processing on the jamming-suppressed time-frequency matrix to obtain the final suppression results. Simulation experiments demonstrate the proposed algorithm exhibiting superior jamming suppression performance, particularly addressing the limitations of traditional methods under low signal-to-noise ratio (SNR) conditions.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105599"},"PeriodicalIF":3.0,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048942","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}
引用次数: 0
AdLU: Adaptive double parametric activation functions 自适应双参数激活函数
IF 3 3区 工程技术
Digital Signal Processing Pub Date : 2025-09-05 DOI: 10.1016/j.dsp.2025.105579
Merve Güney Duman , Sibel Koparal , Neşe Ömür , Alp Ertürk , Erchan Aptoula
{"title":"AdLU: Adaptive double parametric activation functions","authors":"Merve Güney Duman ,&nbsp;Sibel Koparal ,&nbsp;Neşe Ömür ,&nbsp;Alp Ertürk ,&nbsp;Erchan Aptoula","doi":"10.1016/j.dsp.2025.105579","DOIUrl":"10.1016/j.dsp.2025.105579","url":null,"abstract":"<div><div>Activation functions are critical components of neural networks, introducing the necessary nonlinearity for learning complex data relationships. While widely used functions such as ReLU and its variants have demonstrated notable success, they still suffer from limitations such as vanishing gradients, dead neurons, and limited adaptability at various degrees. This paper proposes two novel differentiable double-parameter activation functions (AdLU<span><math><msub><mrow></mrow><mn>1</mn></msub></math></span> and AdLU<span><math><msub><mrow></mrow><mn>2</mn></msub></math></span>) designed to address these challenges. They incorporate tunable parameters to optimize gradient flow and enhance adaptability. Evaluations on benchmark datasets, MNIST, FMNIST, USPS, and CIFAR-10, using ResNet-18 and ResNet-50 architectures, demonstrate that the proposed functions consistently achieve high classification accuracy. Notably, AdLU<span><math><msub><mrow></mrow><mn>1</mn></msub></math></span> improves accuracy by up to 5.5 % compared to ReLU, particularly in deeper architectures and more complex datasets. While introducing some computational overhead, their performance gains establish them as competitive alternatives to both traditional and modern activation functions.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105579"},"PeriodicalIF":3.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048943","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}
引用次数: 0
ADCLoc: A robust and adaptive CSI-based device-free passive indoor localization approach for dynamic environments ADCLoc:一种鲁棒和自适应的基于csi的动态环境无设备被动室内定位方法
IF 3 3区 工程技术
Digital Signal Processing Pub Date : 2025-09-05 DOI: 10.1016/j.dsp.2025.105568
Xinping Rao , Qiangqiang Zhou , Yugen Yi , Gang Lei , Yulei Wu , Yuanlong Cao
{"title":"ADCLoc: A robust and adaptive CSI-based device-free passive indoor localization approach for dynamic environments","authors":"Xinping Rao ,&nbsp;Qiangqiang Zhou ,&nbsp;Yugen Yi ,&nbsp;Gang Lei ,&nbsp;Yulei Wu ,&nbsp;Yuanlong Cao","doi":"10.1016/j.dsp.2025.105568","DOIUrl":"10.1016/j.dsp.2025.105568","url":null,"abstract":"<div><div>The proliferation of emerging sensor technologies and the expansion of the Internet of Things (IoT) have sparked significant interest in advanced indoor localization methodologies. Device-free passive localization through Channel State Information (CSI) fingerprinting has attracted considerable research attention, as it eliminates the need for target participation, rendering it particularly valuable for location-based IoT applications. However, ensuring accuracy in dynamic real-world environments remains challenging due to the inherent trade-off between deployment costs and localization precision. Variations in CSI caused by environmental fluctuations can degrade system performance, underscoring the necessity for robust and adaptable solutions. In this study, we propose ADCLoc, a novel CSI-based device-free passive localization framework specifically designed for dynamic environments. ADCLoc employs a fusion model that integrates spatio-temporal redundancies in CSI amplitude and phase data, thereby enhancing the discriminative capacity of localization fingerprints. Central to ADCLoc is an adaptive convolutional neural network (AdaptCNN) incorporating a meta-learning dual-stream architecture for unsupervised domain adaptation. This design enables continuous adaptation to fluctuating CSI conditions inherent in dynamic environments while maintaining high performance without requiring extensive retraining. A six-day evaluation under controlled environmental modifications—including furniture rearrangement and door/obstacle configuration changes—demonstrates that ADCLoc surpasses existing methods in both localization accuracy and robustness.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105568"},"PeriodicalIF":3.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019100","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}
引用次数: 0
MCSFI: A detection network for photovoltaic panel defect detection of multi-scale content-aware feature integration MCSFI:一种多尺度内容感知特征集成的光伏板缺陷检测网络
IF 3 3区 工程技术
Digital Signal Processing Pub Date : 2025-09-05 DOI: 10.1016/j.dsp.2025.105571
Zijian He , Siyu Li , Genyuan Chen , Lingling Wang
{"title":"MCSFI: A detection network for photovoltaic panel defect detection of multi-scale content-aware feature integration","authors":"Zijian He ,&nbsp;Siyu Li ,&nbsp;Genyuan Chen ,&nbsp;Lingling Wang","doi":"10.1016/j.dsp.2025.105571","DOIUrl":"10.1016/j.dsp.2025.105571","url":null,"abstract":"<div><div>The development of photovoltaic (PV) panel systems not only mitigates pollution caused by fossil fuel combustion but also addresses the growing global demand for sustainable energy. Defect detection in PV panels is critical to ensuring the reliable operation of PV power systems. However, existing methods for defect detection face challenges in balancing computational resource efficiency with detection accuracy. To address these limitations, this article proposes the Multi-Scale Content-Aware Feature Integration (MCSFI) network model, which achieves enhanced detection performance while maintaining a lightweight design. First, the article introduces the SARepVGG module, integrated into both the Backbone and Neck networks, to strengthen the model's ability to represent defect-related features. Second, the article designs a Multi-Scale Context-Aware Feature Enhancement (MFCARAFE) module, which processes outputs from multiple convolutional layers in order to comprehensively aggregate defect features across different scales. This significantly improves detection accuracy for PV panel defects of varying sizes. Third, the article proposes the Adaptive Input Feature Integration Convolution (AIFIC) module, which combines adaptive input feature calibration with dual-path convolutional technique to enhance the model's adaptability to complex scenarios and generalization capabilities. Extensive experiments on the PVEL-AD dataset and Dataset A validate the effectiveness of our approach. Compared with the baseline model, the proposed MCSFI model achieves a 1.3% improvement in mAP on the PVEL-AD dataset while reducing the model weight size by 6.1%. Similar performance achievements are observed on Dataset A. These results demonstrate that our method successfully balances multi-scale defect detection accuracy with computational efficiency, offering a novel solution for practical PV panel defect inspection.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105571"},"PeriodicalIF":3.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010320","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}
引用次数: 0
Robust adaptive CFAR detection using AR-sieve bootstrap and sample autocovariance 基于ar筛自举和样本自协方差的鲁棒自适应CFAR检测
IF 3 3区 工程技术
Digital Signal Processing Pub Date : 2025-09-05 DOI: 10.1016/j.dsp.2025.105574
Chang Qu , Li Yang , Lei Zhen , Xiaoying Wang , Junping Yin
{"title":"Robust adaptive CFAR detection using AR-sieve bootstrap and sample autocovariance","authors":"Chang Qu ,&nbsp;Li Yang ,&nbsp;Lei Zhen ,&nbsp;Xiaoying Wang ,&nbsp;Junping Yin","doi":"10.1016/j.dsp.2025.105574","DOIUrl":"10.1016/j.dsp.2025.105574","url":null,"abstract":"<div><div>Radar target detection remains a critically important research area. This paper models the radar echo data within each range cell as a stationary linear process driven by reversible, independent and identically distributed innovations. Exploiting the distinct inter-pulse correlation structures present under target absent and target present conditions, we propose the sample autocovariance as the detection statistic. Under appropriate theoretical conditions, we establish the validity of the autoregressive (AR) sieve bootstrap for approximating the distribution of this statistic. Adopting a single-sample hypothesis testing framework, we develop an adaptive constant false alarm rate (CFAR) detector, termed the Sample Autocovariance Trimmed CFAR (SACT-CFAR). Specifically, this method operates as follows: the numerical distribution of the sample autocovariance statistic is derived using the AR-sieve bootstrap method. The detection threshold for the cell under test is then determined based on a predefined false alarm probability. Through comprehensive numerical experiments on both simulated and real-world radar data, we benchmark the SACT-CFAR against established target detection methods. Key advantages of our approach include: 1. Superior Performance: Demonstrates higher detection probability, particularly in challenging low signal-to-clutter ratio regimes; 2. Model-Free Practicality: Eliminates the need for explicit derivation of theoretical detection thresholds and explicit statistical clutter modeling; 3. Robust Generality: Exhibits significant adaptability across diverse clutter environment distributions, overcoming the limitations of detectors reliant on specific clutter assumptions.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105574"},"PeriodicalIF":3.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019101","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}
引用次数: 0
System energy efficiency maximization-oriented dual-stage collaborative beamforming design for hybrid intelligent reflecting surface-aided EHCRSNs 面向系统能效最大化的混合智能反射表面辅助ehcrsn双级协同波束形成设计
IF 3 3区 工程技术
Digital Signal Processing Pub Date : 2025-09-05 DOI: 10.1016/j.dsp.2025.105597
Jihong Wang, Hongyu Yang, Yang Li
{"title":"System energy efficiency maximization-oriented dual-stage collaborative beamforming design for hybrid intelligent reflecting surface-aided EHCRSNs","authors":"Jihong Wang,&nbsp;Hongyu Yang,&nbsp;Yang Li","doi":"10.1016/j.dsp.2025.105597","DOIUrl":"10.1016/j.dsp.2025.105597","url":null,"abstract":"<div><div>To tackle the problem of low energy efficiency (EE) caused by the energy harvesting (EH) and data transmission between cognitive radio sensor networks (CRSNs) nodes and the energy source sink via direct links, hybrid intelligent reflecting surface (H-IRS) is incorporated into CRSNs for the first time. H-IRS assists both downlink EH and uplink data communication, and a non-convex optimization problem subject to constraints is formulated to maximize the system EE. To solve this, a dual-stage collaborative beamforming mechanism is proposed, which jointly optimizes the beamforming of both the sink and H-IRS. A grouped alternating optimization strategy is employed to handle the coupling of multiple optimization variables, combined with a low-complexity algorithm that incorporates fractional programming and successive convex approximation. This mechanism progressively transforms the fractional non-convex optimization problem into a convex problem, addressing the challenges of multi-dimensional coupled variable optimization. Simulation results show that with an appropriate number of active reflecting elements and sufficient maximum amplification power budget of the active IRS sub-surface, the proposed mechanism achieves a minimum 10 % improvement ratio in system EE over the baseline mechanisms.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105597"},"PeriodicalIF":3.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048937","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}
引用次数: 0
Recursive feedback-based feature refinement network for camouflaged object detection 基于递归反馈的伪装目标检测特征细化网络
IF 3 3区 工程技术
Digital Signal Processing Pub Date : 2025-09-04 DOI: 10.1016/j.dsp.2025.105577
Yu Liu, Qiang Dai, Haiyu Liao, YaoRui Tang, Xiaohui Luo
{"title":"Recursive feedback-based feature refinement network for camouflaged object detection","authors":"Yu Liu,&nbsp;Qiang Dai,&nbsp;Haiyu Liao,&nbsp;YaoRui Tang,&nbsp;Xiaohui Luo","doi":"10.1016/j.dsp.2025.105577","DOIUrl":"10.1016/j.dsp.2025.105577","url":null,"abstract":"<div><div>Accurate segmentation of camouflaged objects from camouflaged images is a valuable but challenging task. Although some achievements have been made in camouflaged object detection, several challenges remain. Camouflaged objects, occluded by the foreground or embedded in the background, exhibit unclear boundaries and high similarity with their surroundings, especially when multiple objects are involved. To confront these challenges, a novel Recursive Feedback-based Feature Refinement Network (RFF-Net) is proposed for camouflaged object detection. Specifically, a boundary detection network is proposed first, which is used to generate boundary-aware features. Then, the extracted boundary features are integrated into the segmentation network. In particular, we design a segmentation network that utilizes recursive feedback from higher-level features to guide lower-level features, thereby gradually refining the segmentation results for camouflaged objects. Furthermore, a Boundary Feature Aggregation Module (BFAM) is proposed to fuse boundary information with multi-level features, which can enhance the multi-level backbone features to generate finer segmentation results. Further, an Interclass Discrepancy Enhancement Module (IDEM) is proposed to amplify the interclass differences between a camouflaged object and its surroundings, which can make full use of multi-scale and contextual information to highlight the location of the camouflaged object. Quantitative and qualitative experiments on four challenging benchmark datasets prove the outperformance of our RFF-Net compared to various state-of-the-art camouflaged object segmentation models.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105577"},"PeriodicalIF":3.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010319","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}
引用次数: 0
Unsupervised dual-branch cross-domain person re-identification based on domain-invariant features 基于域不变特征的无监督双分支跨域人再识别
IF 3 3区 工程技术
Digital Signal Processing Pub Date : 2025-09-04 DOI: 10.1016/j.dsp.2025.105567
Bin Hu, Guofeng Zou, Yushan Chen, Zhiwei Huang, Guixia Fu
{"title":"Unsupervised dual-branch cross-domain person re-identification based on domain-invariant features","authors":"Bin Hu,&nbsp;Guofeng Zou,&nbsp;Yushan Chen,&nbsp;Zhiwei Huang,&nbsp;Guixia Fu","doi":"10.1016/j.dsp.2025.105567","DOIUrl":"10.1016/j.dsp.2025.105567","url":null,"abstract":"<div><div>To address the distribution discrepancy between the source and target domains caused by camera-specific style variations, we propose a unsupervised dual-branch cross-domain person re-identification framework based on domain-invariant feature learning. Specifically, during the source domain pre-training phase, considering the distribution shift induced by inter-camera style differences, we treat each camera as an independent style domain. CycleGAN is employed to perform camera-style transfer, which significantly enhances the diversity of training samples and alleviates inter-domain distribution bias. To simultaneously capture fine-grained local details and high-level semantic context, we place the IBN and Non-local modules in Layer2 and Layer3 of the network. Additionally, a fixed exponent GeM pooling strategy is adopted to improve both the discriminability and generalizability of the learned features. During the target domain adaptation stage, in order to suppress the noise introduced by clustering-generated pseudo labels, a dual-branch symmetric architecture is constructed. An Exponential Moving Average model is maintained to generate soft pseudo labels. Using complementary supervision between hard and soft labels, our method effectively mitigates label noise and enhances robustness. Extensive experiments conducted on three widely used datasets (Market1501, DukeMTMC-reID, and MSMT17) demonstrate the effectiveness of the proposed method in both unsupervised domain adaptation and purely unsupervised person re-identification tasks.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105567"},"PeriodicalIF":3.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048934","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}
引用次数: 0
An occlusion light field sparse Bayesian learning model for view synthesis 一种遮挡光场稀疏贝叶斯学习模型用于视图合成
IF 3 3区 工程技术
Digital Signal Processing Pub Date : 2025-09-04 DOI: 10.1016/j.dsp.2025.105573
Weiyan Chen , Changjian Zhu , Shan Zhang
{"title":"An occlusion light field sparse Bayesian learning model for view synthesis","authors":"Weiyan Chen ,&nbsp;Changjian Zhu ,&nbsp;Shan Zhang","doi":"10.1016/j.dsp.2025.105573","DOIUrl":"10.1016/j.dsp.2025.105573","url":null,"abstract":"<div><div>Given a set of captured views with known positions, our goal is to obtain different views from new positions. However, synthesizing novel views from new positions is challenging since occlusion in real-world scenes is complex and ubiquitous. In this paper, we describe a method for synthesizing a novel view of an occluded scene, that is, an occlusion light field (OLF) sparse Bayesian learning network (OLiFi-Net). Specifically, we break down the process into OLF parameterization and interpolation reconstruction components. For the first component, we utilize a sparse Bayesian learning approach to establish an OLF expression. This expression can then be used to derive the convolution interpolation kernel function. For the second component, the kernel function can be applied to the circular convolutional network to synthesize novel views in a variety of occlusion situations. The reconstruction results on extensive datasets validate our model and demonstrate that we can render views for both occluded and nonoccluded scenes.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105573"},"PeriodicalIF":3.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019099","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}
引用次数: 0
Doppler resilient complementary sequence set design via a model driven deep learning method 基于模型驱动深度学习方法的多普勒弹性互补序列集设计
IF 3 3区 工程技术
Digital Signal Processing Pub Date : 2025-09-03 DOI: 10.1016/j.dsp.2025.105583
Xiangqing Xiao , Hua Wang , Jinfeng Hu , Yuankai Wang , Jun Liu , Kai Zhong , Huiyong Li
{"title":"Doppler resilient complementary sequence set design via a model driven deep learning method","authors":"Xiangqing Xiao ,&nbsp;Hua Wang ,&nbsp;Jinfeng Hu ,&nbsp;Yuankai Wang ,&nbsp;Jun Liu ,&nbsp;Kai Zhong ,&nbsp;Huiyong Li","doi":"10.1016/j.dsp.2025.105583","DOIUrl":"10.1016/j.dsp.2025.105583","url":null,"abstract":"<div><div>Doppler-resilient complementary sequence set (CSS) design is a key technology in radar systems, characterized by its inherently non-convex bivariate nature with multiple complex constraints. Existing methods mainly solve it through relaxation, which inevitably introduces relaxation errors. It is worth noting that the multi-constraint formulation can be transformed into an unconstrained optimization through projection onto a unified constraint space (UCS). Within this UCS, the bivariate problem becomes directly tractable via parallel gradient computation, while the original objective function naturally serves as a loss function for training a deep learning network. Motivated by above points, a relaxation-free parallel gradient projection network (PGPN) method is proposed. The proposed PGPN method begins by constructing a UCS that incorporates all constraints, effectively reframing the problem as an unconstrained optimization. A parallel gradient projection (PGP) algorithm is then derived to compute the bivariate gradients efficiently. This PGP algorithm is subsequently unfolded into network layers, with the objective function repurposed as the network’s loss function and adaptive step size updates enabling parallel optimization. The key innovation of this research is that unifying constrained waveform-filter optimization via a constraint-to-unconstrained transformation, parallel gradient-based joint optimization, and deep learning-embedded adaptive tuning, enabling high-fidelity waveform design in dynamic electromagnetic environments. Simulation results show that the signal-to-interference ratio (SIR) of the proposed method achieves better Doppler resilience compared to L-BFGS [18], MMCSR [21], and GP [27], while also enabling better control of the signal-to-noise ratio loss (SNRL).</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105583"},"PeriodicalIF":3.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048938","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}
引用次数: 0
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