Signal classification plays a pivotal role in modern digital communications by ensuring efficient and secure data transmission across critical tasks, such as automatic modulation classification (AMC) and wireless technology classification (WTC). In recent years, the advent of Deep Learning (DL) has revolutionized signal classification by automating feature extraction and significantly improving classification accuracy in dynamic and noisy environments. Despite these advancements, effectively addressing the challenges of multi-task signal classification in complex scenarios remains an open problem. To tackle this, this paper presents a novel knowledge-and-data-driven multi-scale gated multi-layer perceptron (KDD-MSGMLP) framework, specifically designed to concurrently handle AMC and WTC tasks. The framework combines environmental side information as prior knowledge with advanced deep features, thereby substantially enhancing both robustness and accuracy in classification. At its core, the KDD-MSGMLP framework is powered by an innovative MSGMLP backbone network. This backbone effectively integrates multi-scale convolution with residual connections to enable shared feature embedding across tasks, while its gated multi-layer perceptron modules focus on refining task-specific features. Such a design ensures robust and efficient signal classification, making it well-suited for complex wireless communication environments. Simulation results demonstrate that the proposed framework achieves performance improvements of over 1% in AMC and between 2% and 4% in WTC. Moreover, it shows significant potential for enhancing performance and efficiency in complex wireless scenarios. The codes supporting this work are available on GitHub1.

In recent years, research in the field of hyperspectral image classification (HSIC) has increasingly focused on the open-set problem. Open-set classification demands not only accurately classifying the known categories but also identifying the unknown samples that are not labeled or included within the training data during testing stage. Existing open-set methods often suffer from misclassification between the known and unknown categories due to their inadequate utilization of metric space. Moreover, relying on a single threshold strategy performs poorly for identifying unknown categories in complex open environments. In this paper, a fractional domain information enhanced hyperspherical proto-type learning method (FrHSPL) is proposed for hyperspectral image open-set classification. FrHSPL develops a hyperspherical prototype learning (HSPL) strategy that ensures the features of known categories are uniformly distributed on the hypersphere. Therefore, HSPL can effectively enhance inter-class separability and optimize the exploitation of metric space. Subsequently, to enhance the discrimination capability of spectral features, a frequency-spatial-spectral information aggregation module is devised to deeply integrate fractional domain information with spatial and spectral information. Finally, an open-set recognition module is designed to identify unknown categories by using the prototypes of each known category along with the corresponding prototype radii. Extensive experiments on four common HSI datasets indicate that the proposed FrHSPL exhibits superior performance in comparison with both closed-set and open-set methods.

Extracting spatial-spectral joint features has become a critical approach for improving model classification performance in the field of hyperspectral image classification (HSIC). However, existing methods fail to fully exploit nonlinear spatial-spectral information. Unlike traditional convolutional neural networks (CNNs), graph convolutional neural networks (GCNs) can extract nonlinear spatial information. Nevertheless, both methods lack an accurate measurement of local neighborhood information, leading to blurred classification boundaries for ground objects. Additionally, the high-dimensional nature of hyperspectral data results in poor generalization and redundant information of trained models. To address these three issues, a nonlinear weighted graph convolution network based on manifold geometric regularization (MGR-NWGCN) method is devised for HSIC. Specifically, a nonlinear weighted graph convolution (NWGCN) module is designed, which utilizes a Graph-in-Graph structure based on cosine similarity-based normalized weighted graph convolution to extract nonlinear spatial-spectral information. Then, the manifold curvature regularization (C-MGR) module is implemented to improve the accuracy of similarity measurement and to enhance the generalization ability of the model, which constrains the model to form flatter feature manifold surfaces. Finally, the manifold intrinsic dimensionality regularization (ID-MGR) module is developed with the aim of eliminating redundant information, which embeds noise onto the surface of a low-dimensional manifold. The superior classification performance and robustness of the proposed MGR-NWGCN method are validated through extensive experiments on four datasets, with comparisons conducted against nine methods.

With the rapid development of low-end maritime devices and shipborne sensors, traditional single-modal recognition can no longer meet the demand for high accuracy in maritime environment. As a result, multimodal learning, which integrates data from different sensors, has gradually become the main approach for maritime target recognition. However, due to variations in the environments and platforms where data is collected, the quantity of useful information provided by each modality differs, and certain modalities may even introduce noise. This discrepancy adversely affects the performance of multimodal fusion recognition. However, most existing multimodal maritime recognition methods overlook these differences, which constrains the performance of the recognition models. To address this concern, we propose a multi-modal contribution evaluation fusion network (MCEF-NET) to achieve efficiency-enhanced fusion for multimodal maritime target recognition. In this model, a Feature Filter Module (FFM) is introduced to effectively suppress irrelevant information, mitigate distribution discrepancies between modalities, and enhance the robustness of multimodal feature extraction. Furthermore, we design a Contribution-Rating Fusion (CRF) mechanism that dynamically allocates fusion weights according to the contribution of each modality’s features, thereby minimizing the influence of low-value modalities on the final fusion performance. The MCEF-NET was evaluated on the publicly available VAIS maritime infrared-visible multimodal dataset, exhibiting superior accuracy and computational efficiency compared to existing state-of-the-art methods.

As Industry 4.0 continues to evolve, the integration of cyber-physical systems (CPS) into modern engineering systems marks a significant paradigm shift that not only enhances operational efficiency but also opens up new possibilities for innovation and improved quality of life across various sectors. In intricate communication environments, the precise identification of device identities within CPS holds significant importance for augmenting reliability and robustness. For this purpose, we introduce a robust method for specific emitter identification that utilizes a deep residual shrinkage network, aimed at enhancing the model's ability to accurately recognize emitters, even when operating under conditions of low signal-to-noise ratios. This is an end-to-end recognition method that reduces the dependence on expert knowledge. Through the utilization of specially crafted subnetworks, adaptive thresholding is employed to enable each in-phase and quadrature (IQ) signal to possess its unique set of thresholds. The proposed approach reduces noise influence on the model by incorporating a soft threshold within the nonlinear transformation layer of the deep architecture. Experimental results using real-world data demonstrate that this method surpasses the performance of commonly utilized state-of-the-art specific emitter identification models.

In this letter, we provide the target signal enhancement method based on deep learning for weak target detection. First, the proposed method fully considers the nature characteristic of radar complex echoes and exploits the complex-valued neural networks. Then, the architecture of weak target enhancement complex-valued generative adversarial network (WTE-CGAN) is proposed. More specifically, the generator loss function of generative adversarial network (GAN) is modified, which can be used to reflect the difference between the generated target signal by the generator and the label signal. To keep the training stability of the proposed method, a gradient penalty factor is randomly added to every sample, which embodies the loss function of discriminator. Finally, simulation and measured experiments are given to demonstrate the effectiveness of the proposed method compared with other methods, and it has a significant signal enhancement effect on weak targets.

Traditional synthetic aperture radar (SAR) image sea-land segmentation algorithms overlook the ship distribution priori-information provided by the automatic identification system (AIS) data, resulting in poor segmentation performance in complex environments such as ports, marine wetlands, beaches, and other sea-land boundaries. To address the above issues, this article comprehensively uses dual-polarization (VV and VH) SAR images and AIS data as the data source, and it specifically proposes a novel pyramid vision transformer (PVT) assisted by the long-time AIS data (AIS-PVT) for sea-land segmentation. AIS-PVT is the first attempt to integrate the ship distribution density priori-information, provided by the long-time AIS data, into the PVT network, thus the multiscale features of the sea and land can be better distinguished. In the decoding stage, we design a feature filter module (FFM). It aggregates features separately along two spatial directions from the skip connections, enhancing the representation of objects of interest while reducing the influence of redundant information. Furthermore, we develop a boundary-pixel-aware function to steer the model training process, allowing AIS-PVT to concentrate more on the neighborhood information of boundary pixels. Importantly, the AIS-PVT method captures global multiscale information and enhances the model’s data fusion capability. The conclusive experimental results demonstrate the superior performance of our approach in sea-land segmentation tasks, outperforming other state-of-the-art (SOTA) techniques.

This article presents two amodal instance segmentation (AIS) datasets in the field of remote sensing: the multiview building dataset for the Zurich region (MVB-Zurich) and the multisize building dataset for the Dortmund region (MSB-Dortmund). Additionally, a new AIS network framework, named FE-RSBL-AmodalNet, is proposed, which is based on feature enhancement and remote sensing boundary loss. Instance segmentation has emerged as a popular approach for building extraction in recent years. However, a limitation of using such algorithms for building extraction is the inability to predict the invisible areas. Consequently, the extracted building contours are incomplete, which hampers certain applications relying on accurate building extraction. To address this limitation, AIS has emerged as a promising research field. Unfortunately, there is currently a lack of datasets available for developing AIS algorithms in the field of remote sensing, which poses a barrier to the widespread application of AIS in this domain. This article introduces two AIS datasets specifically designed for remote-sensing buildings. The datasets consist of images captured from tilted views using a tilt photography system, resulting in a significant presence of occluded areas within the images. Moreover, the MVB-Zurich dataset comprises aerial images captured from five different viewpoints, while the MSB-Dortmund dataset encompasses diverse buildings, including garages and residences, with varying sizes. The multiview and multisize attributes of these datasets offer enhanced research opportunities. Furthermore, a new AIS framework, specifically designed for remote sensing buildings, was proposed with the aim of accurately predicting complete building contours.

Radio frequency fingerprinting (RFF) serves as a distinctive hardware trait in transmitters, forming the cornerstone of transmitter identification. While recent advancements led to significant improvements in identification accuracy, these developments also inadvertently simplify the process for adversaries to detect our transmitters. This vulnerability is particularly concerning in secure communications, as the exposure of device information could potentially result in the compromise of communication content, posing significant security threats. To counteract such risks and safeguard transmitters against unauthorized identification, this paper proposes a novel RFF concealment (RFFC) method based on IQ imbalance compensation and digital pre-distortion (DPD) techniques. This method not only effectively conceals the RFF, preventing malicious detection of the transmitter, but also enhances the system’s linearization performance. The effectiveness of the proposed RFFC framework is validated through MATLAB Simulink and a software and hardware test platform. Experimental results show that using the blind generalized linear structure-based IQ imbalance and deep neural network (DNN)-based PA nonlinearity joint concealment method performs best, reducing transmitter identification accuracy to only 17% under various signal-to-noise ratio conditions. Additionally, this method performs the best in system linearization performance.

The shape reconstruction of buildings based on LiDAR point clouds is extremely significant in remote sensing. In recent years, reconstruction methods based on the implicit network have been widely used in object-level shape reconstruction. However, the incompleteness and sparsity of airborne LiDAR scanning point clouds will lead to poor reconstruction results. To solve this problem, GEOP-Net: an implicit modeling framework embedded with high-dimensional geometric features, is proposed in this letter. Firstly, the geometric encoding module added to extract high-dimensional features enhances the feature extraction ability of the network to the detailed structures. The point clouds of buildings in Zurich are collected and used to evaluate the performance of the proposed method. The experimental results show that the proposed method have better accuracy than the existing methods, so it provides a new research idea for building reconstruction.

Decentralized Federated Learning (DeFL) plays a critical role in improving effectiveness of training and has been proved to give great scope to the development of edge computing. However, on the one hand, inaccessibility of private data and excessively exploiting the data throughout the learning process have become a public concern, and on the other hand the connections between server-less edge devices are always varying due to the mobility of edge intelligent devices. To address the above issues, we propose aPrivacy-Enhanced -Dynamic -Decentralized -Federated -Learning algorithm called PED$ ^{2}$FL in a dynamic edge environment. We design the PED$ ^{2}$FL under the analog transmission scheme, where mobile edge devices transmit privacy preserving data simultaneously and accomplish efficient information aggregation with doubly-stochastic adjacent matrices. With thorough analysis, it can be demonstrated that PED$ ^{2}$FL satisfies$(\epsilon,\delta)$-differential privacy while the per-device privacy budget decays exponentially with the number of the neighbors, which greatly improved the data utility compared to the fixed budget in the orthogonal transmission strategy. PED$ ^{2}$FL has the same convergence rate$\mathcal {O}(\sqrt{\frac{1}{KN}})$as the non-private decentralized learning algorithm D-PSGD without enhanced privacy protection, where$K$and$N$are the total iterations and the number of nodes, respectively. Extensive experiments show that algorithm PED$ ^{2}$FL also performs well with real-world settings.

Synthetic aperture radar (SAR) ship detection methods have achieved remarkable progress in recent years. However, unlike RGB images, the characteristics of SAR imaging will result in non-intuitive feature representations. Furthermore, due to the insufficient data of SAR images, existing methods relying on plenty of labeled SAR images may be hard to achieve promising performance. To address the aforementioned issues, a cross-modality feature transfer (CMFT) method is proposed in this article, which enhances feature representations in the SAR modality by transferring rich knowledge in the RGB modality. First, we propose a multilevel modality alignment network (MMAN), which encourages the model to effectively learn modality-invariant features and alleviate the large cross-modality discrepancies by aligning features from multilevels (scene level, local level, global level, and instance level). Second, to address the underperformance of samples with non-intuitive features in the modality alignment, we introduce a hard-sample supervision module (HSM) in the stage of feature extraction, which can thoroughly exploit the feature of hard-to-align samples by giving more optimization energy for them. Third, to enhance the discriminability of instance-level features, a feature complementary module (FCM) is customized to fully explore the potential complementary clues between instance-level features and context information for the instance-level feature alignment. Extensive experimental results demonstrate that the CMFT outperforms the state-of-the-art detectors. Compared to the baseline model, CMFT improves the accuracy by 3.1% mean average precision (mAP) on the SSDD dataset and 3.4% mAP on the HRSID dataset, demonstrating its superior SAR ship detection performance.

Existing crowd counting approaches predominantly perform well on the training-testing protocol. However, due to large style discrepancies not only among images but also within a single image, they suffer from obvious performance degradation when applied to unseen domains. In this paper, we aim to design a generalizable crowd counting framework which is trained on a source domain but can generalize well on the other domains. To reach this, we propose a gated ensemble learning framework. Specifically, we first propose a diverse fine-grained style attention model to help learn discriminative content feature representations, allowing for exploiting diverse features to improve generalization. We then introduce a channel-level binary gating ensemble model, where diverse feature prior, input-dependent guidance and density grade classification constraint are implemented, to optimally select diverse content features to participate in the ensemble, taking advantage of their complementary while avoiding redundancy. Extensive experiments show that our gating ensemble approach achieves superior generalization performance among four public datasets. Codes are publicly available athttps://github.com/wdzhao123/DCSL.

Fine-grained object classification (FGOC) focuses on identifying subcategories of objects, which is crucial in military and civilian. Existing FGOC methods primarily focus on high-resolution aerial images, limiting their application on low-resolution (LR) FGOC that is a more realistic setting, especially on resource-constrained satellite devices. It is more challenging to deal with LR FGOC since objects’ details are blurred or missing. Addressing this issue, we make the first attempt to explore LR FGOC and propose a novel pipeline based on two technical insights: 1) feature balance strategy discriminatively integrates super-resolution weak and strong detailed presentations into coarse features of LR aerial images, achieving a feature balance to avoid that the weak detailed presentations are inhibited by the strong ones and 2) iterative interaction mechanism alternately refines feature details of the discriminative ship regions and optimizes the performance of FGOC. Moreover, we build a low-resolution fine-grained object (LFS) dataset to promote further study and evaluation. Extensive experiments on the proposed LFS dataset and the other three object datasets of DOTA, FS23, and HRSC2016 demonstrate that our method outperforms state-of-the-art algorithms. Dataset and code are publicly available athttps://github.com/wdzhao123/FBNet.

To address radar/ESM track association problem in formation in the presence of systematic biases, an anti-bias track association algorithm based on hierarchical clustering analysis is proposed. The influence of formation and systematic biases on association is analyzed first. In order to eliminate the effect of biases, the relative bearing bias between radar and ESM is estimated by hierarchical clustering for distance vectors in MPC. Finally, anti-bias track association is achieved based on the global optimal assignment. Simulation results indicate the proposed algorithm outperforms the state-of-the-art approaches.

Considering the convergence rate is a very important issue as distributed sensors networks usually consist of low-powered wireless devices and speeding up the consensus convergence rate is also important to reduce the number of messages exchanged among neighbors, a new adaptive method for weight assignment of communication links between sensor nodes is proposed based on the dynamic network topology. Based on the adaptive weight assignment method, an improved Kalman consensus filter (KCF) named IKCF is tailored in this letter for distributed state estimation in sensor networks with cluster structure. Furthermore, the experiments demonstrate the adaptive weight assignment method is effective for distributed state estimation when the sensor network is sparsely deployed. In addition, the simulation results also validate the superior performance of the new algorithm and show that IKCF is an excellent algorithm for multi-clusters sensor networks. And there is no additional communication overhead in IKCF because only some local knowledge is used to autonomously calculate the adaptive consensus rate parameter for each node.