Information Fusion最新文献_第8页

GeoCraft: A Diffusion Model-based 3D Reconstruction Method driven by image and point cloud fusion georaft：一种基于扩散模型的图像和点云融合驱动的三维重建方法

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-13 DOI: 10.1016/j.inffus.2026.104149

Weixuan Ma , Yamin Li , Chujin Liu , Hao Zhang , Jie Li , Kansong Chen , Weixuan Gao

With the rapid development of technologies like virtual reality (VR), autonomous driving, and digital twins, the demand for high-precision and realistic multimodal 3D reconstruction has surged. This technology has become a core research focus in computer vision and graphics due to its ability to integrate multi-source data, such as 2D images and point clouds. However, existing methods face challenges such as geometric inconsistency in single-view reconstruction, poor point cloud-to-mesh conversion, and insufficient multimodal feature fusion, limiting their practical application. To address these issues, this paper proposes GeoCraft, a multimodal 3D reconstruction method that generates high-precision 3D models from 2D images through three collaborative stages: Diff2DPoint, Point2DMesh, and Vision3DGen. Specifically, Diff2DPoint generates an initial point cloud with geometric alignment using a diffusion model and projection feature fusion; Point2DMesh converts the point cloud into a high-quality mesh using an autoregressive decoder-only Transformer and Direct Preference Optimization (DPO); Vision3DGen creates high-fidelity 3D objects through multimodal feature alignment. Experiments on the Google Scanned Objects (GSO) and Pix3D datasets show that GeoCraft excels in key metrics. On the GSO dataset, its CMMD is 2.810 and FID_CLIP is 26.420; on Pix3D, CMMD is 3.020 and FID_CLIP is 27.030. GeoCraft significantly outperforms existing 3D reconstruction methods and also demonstrates advantages in computational efficiency, effectively solving key challenges in 3D reconstruction.The code is available at https://github.com/weixuanma/GeoCraft.

随着虚拟现实（VR）、自动驾驶、数字孪生等技术的快速发展，对高精度、逼真的多模态3D重建的需求激增。该技术由于能够集成二维图像和点云等多源数据，已成为计算机视觉和图形学领域的核心研究热点。然而，现有方法存在单视图重构几何不一致、点云-网格转换差、多模态特征融合不足等问题，限制了其实际应用。为了解决这些问题，本文提出了GeoCraft，这是一种多模态3D重建方法，通过三个协作阶段：Diff2DPoint， Point2DMesh和Vision3DGen，从2D图像生成高精度3D模型。具体来说，Diff2DPoint使用扩散模型和投影特征融合生成具有几何对齐的初始点云；Point2DMesh使用自回归解码器转换器和直接偏好优化（DPO）将点云转换成高质量的网格；Vision3DGen通过多模态特征对齐创建高保真3D对象。在谷歌扫描目标（GSO）和Pix3D数据集上的实验表明，GeoCraft在关键指标上表现优异。在GSO数据集上，其CMMD为2.810，FIDCLIP为26.420；在Pix3D上，CMMD为3.020，FIDCLIP为27.030。GeoCraft大大优于现有的三维重建方法，并且在计算效率方面也显示出优势，有效地解决了三维重建中的关键挑战。代码可在https://github.com/weixuanma/GeoCraft上获得。

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引用次数: 0

GC-Fed: Gradient centralized federated learning with partial client participation GC-Fed：部分客户参与的梯度集中式联邦学习

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-13 DOI: 10.1016/j.inffus.2026.104148

Jungwon Seo , Ferhat Ozgur Catak , Chunming Rong , Kibeom Hong , Minhoe Kim

Federated Learning (FL) enables privacy-preserving multi-source information fusion (MSIF) but suffers from client drift in highly heterogeneous data settings. Many existing approaches mitigate drift by providing clients with common reference points, typically derived from past information, to align objectives or gradient directions. However, under severe partial participation, such history-dependent references may become unreliable, as the set of client data distributions participating in each round can vary drastically. To overcome this limitation, we propose a method that mitigates client drift without relying on past information by constraining the update space through Gradient Centralization (GC). Specifically, we introduce Local GC and Global GC, which apply GC at the local and global update stages, respectively, and further present GC-Fed, a hybrid formulation that generalizes both. Theoretical analysis and extensive experiments on benchmark FL tasks demonstrate that GC-Fed effectively alleviates client drift and achieves up to 20 % accuracy improvement under data heterogeneous and partial participation conditions.

联邦学习（FL）支持保护隐私的多源信息融合（MSIF），但在高度异构的数据设置中存在客户端漂移问题。许多现有的方法通过向客户提供公共参考点（通常来自过去的信息）来调整目标或梯度方向，从而减轻了漂移。然而，在严重的部分参与下，这种依赖历史的引用可能变得不可靠，因为参与每一轮的客户端数据分布集可能会有很大的变化。为了克服这一限制，我们提出了一种方法，通过梯度集中化（GC）限制更新空间，在不依赖过去信息的情况下减轻客户端漂移。具体来说，我们介绍了本地GC和全局GC，它们分别在本地和全局更新阶段应用GC，并进一步提出了GC- fed，这是一种推广两者的混合公式。对基准FL任务的理论分析和大量实验表明，GC-Fed有效缓解了客户端漂移，在数据异构和部分参与条件下，准确率提高了20%。

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引用次数: 0

SymUnet-DynCFC: Multimodal MRI fusion for robust cartilage segmentation and clinically confirmed moderate-to-severe KOA diagnosis SymUnet-DynCFC：多模态MRI融合稳健软骨分割和临床证实的中重度KOA诊断

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-12 DOI: 10.1016/j.inffus.2026.104145

Li Li , Jianbing Ma , Beiji Zou , Hao Xu , Shenghui Liao , Wenyi Xiong , Liqiang Zhi

Knee osteoarthritis (KOA) is a globally prevalent degenerative joint disorder. A central challenge in its automated diagnosis is the efficient fusion of multimodal MRI data. This fusion aims to enhance the accuracy and generalizability of clinical cartilage segmentation, while simultaneously minimizing healthcare resource consumption. Therefore, this study introduces dynamic confidence fuzzy control (DynCFC) within the symmetric unet architecture (SymUnet), referred to as SymUnet-DynCFC, which is designed to enhance the accuracy and robustness of cartilage segmentation. Firstly, the SymUnet architecture is developed, with separate inputs from T1W and T2W modalities to facilitate comprehensive segmentation evaluation. Secondly, the DynCFC mechanism is implemented to compute the optimal weighting for each modality, enabling the fusion and optimization of multimodal features. Finally, the performance of the proposed SymUnet-DynCFC method is evaluated on clinical datasets from a multi-campus hospital system. Experimental results show that SymUnet-DynCFC achieves better segmentation performance than the baselines, with mean Dice, IoU, and HD95 values of 87.96 %, 79.93 %, and 1.29, respectively. In particular, SymUnet-DynCFC exhibits improved robustness compared to the baseline methods. This may facilitate automated cartilage segmentation in clinical workflows and could support the assessment of moderate-to-severe KOA by detecting outlier metrics.

膝骨关节炎（KOA）是一种全球流行的退行性关节疾病。其自动化诊断的核心挑战是多模态MRI数据的有效融合。这种融合旨在提高临床软骨分割的准确性和通用性，同时最大限度地减少医疗资源消耗。因此，本研究在对称unet架构（SymUnet）中引入动态置信度模糊控制（DynCFC），简称SymUnet-DynCFC，旨在提高软骨分割的准确性和鲁棒性。首先，开发了SymUnet架构，从T1W和T2W模式中分离输入，以方便全面的分割评估。其次，采用DynCFC机制计算各模态的最优权重，实现多模态特征的融合和优化；最后，在多校区医院系统的临床数据集上对所提出的SymUnet-DynCFC方法进行了性能评估。实验结果表明，SymUnet-DynCFC的分割性能优于基线，Dice均值为87.96%，IoU均值为79.93%，HD95均值为1.29。特别是，与基线方法相比，SymUnet-DynCFC表现出更好的鲁棒性。这可以促进临床工作流程中的自动软骨分割，并可以通过检测异常指标来支持中度至重度KOA的评估。

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引用次数: 0

Data science: a natural ecosystem 数据科学：一个自然生态系统

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-12 DOI: 10.1016/j.inffus.2025.104113

Emilio Porcu , Roy El Moukari , Laurent Najman , Francisco Herrera , Horst Simon

This manuscript provides a systemic and data-centric view of what we term essential data science, as a natural ecosystem with challenges and missions stemming from the fusion of data universe with its multiple combinations of the 5D complexities (data structure, domain, cardinality, causality, and ethics) with the phases of the data life cycle. Data agents perform tasks driven by specific goals. The data scientist is an abstract entity that comes from the logical organization of data agents with their actions. Data scientists face challenges that are defined according to the missions. We define specific discipline-induced data science, which in turn allows for the definition of pan-data science, a natural ecosystem that integrates specific disciplines with the essential data science. We semantically split the essential data science into computational, and foundational. By formalizing this ecosystemic view, we contribute a general-purpose, fusion-oriented architecture for integrating heterogeneous knowledge, agents, and workflows-relevant to a wide range of disciplines and high-impact applications.

本文提供了一个系统的和以数据为中心的观点，我们称之为基本数据科学，作为一个自然生态系统，其挑战和任务源于数据宇宙与5D复杂性（数据结构、领域、基数、因果关系和伦理）的多种组合与数据生命周期阶段的融合。数据代理执行由特定目标驱动的任务。数据科学家是一个抽象的实体，它来自数据代理及其操作的逻辑组织。数据科学家面临的挑战是根据任务定义的。我们定义了特定学科诱导的数据科学，这反过来又允许定义泛数据科学，这是一个将特定学科与基本数据科学集成在一起的自然生态系统。我们从语义上将基本数据科学分为计算型和基础型。通过形式化这个生态系统视图，我们提供了一个通用的、面向融合的体系结构，用于集成异构知识、代理和工作流程——与广泛的学科和高影响力的应用相关。

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引用次数: 0

Arbitrary-scale spatial-spectral fusion using kernel integral and progressive resampling 使用核积分和渐进重采样的任意尺度空间光谱融合

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-12 DOI: 10.1016/j.inffus.2026.104143

Wei Li, Honghui Xu, Yueqian Quan, Zhe Chen, Jianwei Zheng

Benefiting from the booming deep learning techniques, spatial-spectral fusion (SSF) is considered as an ideal alternative to break the traditions of acquiring hyperspectral images (HSI) with costly devices. Yet with the remarkable progress, current solutions necessitate training and storing multiple models for different scaling factors. To overcome this dilemma, we propose a spatial-spectral fusion neural operator (SFNO) to perform arbitrary-scale SSF within the operator learning framework. Specifically, SFNO approaches the problem from the perspective of approximation theory by embedding the features of two degraded functions into a high-dimensional latent space through pointwise convolution layers, thereby capturing richer spectral feature information. Consequently, the mapping between function spaces is approximated via the Galerkin integral (GI) mechanism, which culminates in a final dimensionality reduction step to produce a high-resolution HSI. Moreover, we propose a progressive resampling integration (PR) that resamples the integrand’s domain in the triple kernel integration to provide non-local multi-scale information. The synergistic action of both integration mechanisms enables SFNO to effortlessly handle magnification factors it never encountered during training. Extensive experiments on the CAVE, Chikusei, Pavia Centre, Harvard, and real-world datasets demonstrate that our SFNO delivers substantial improvements over existing state-of-the-art methods. In particular, under the 8× upsampling setting on the CAVE, Chikusei, and Pavia Centre datasets, SFNO surpasses the second-best model by 0.56 dB, 1.05 dB, and 0.72 dB in PSNR, respectively. Our code is publicly available at https://github.com/weili419/SFNO.

得益于蓬勃发展的深度学习技术，空间光谱融合（SSF）被认为是打破使用昂贵设备获取高光谱图像（HSI）传统的理想替代方案。然而，随着显著的进步，目前的解决方案需要为不同的比例因子训练和存储多个模型。为了克服这一困境，我们提出了一种空间-光谱融合神经算子（SFNO）在算子学习框架内执行任意尺度的SSF。具体而言，SFNO从近似理论的角度解决问题，通过点向卷积层将两个退化函数的特征嵌入到高维潜在空间中，从而捕获更丰富的光谱特征信息。因此，函数空间之间的映射通过伽辽金积分（GI）机制进行近似，该机制在最终降维步骤中达到高潮，从而产生高分辨率的HSI。此外，我们提出了一种渐进式重采样积分（PR），在三核积分中对被积者的域进行重采样，以提供非局部的多尺度信息。两种整合机制的协同作用使SFNO能够毫不费力地处理在训练中从未遇到过的放大因素。在CAVE、Chikusei、Pavia中心、哈佛大学和现实世界的数据集上进行的大量实验表明，我们的SFNO比现有的最先进的方法有了实质性的改进。在CAVE、Chikusei和Pavia Centre数据集的8倍上采样设置下，SFNO的PSNR分别比次优模型高0.56 dB、1.05 dB和0.72 dB。我们的代码可以在https://github.com/weili419/SFNO上公开获得。

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引用次数: 0

Fusing time- and frequency-domain information for effort-independent lung function evaluation using oscillometry 融合时间和频域信息，使用振荡法进行不依赖于努力的肺功能评估

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-11 DOI: 10.1016/j.inffus.2026.104147

Sunxiaohe Li , Dongfang Zhao , Zirui Wang , Hao Zhang , Pang Wu , Zhenfeng Li , Lidong Du , Xianxiang Chen , Hongtao Niu , Xiaopan Li , Jingen Xia , Ting Yang , Peng Wang , Zhen Fang

Current methods for evaluating lung function require substantial patient cooperation and rigorous quality control. In contrast, impulse oscillometry (IOS) is a promising alternative that can measure lung mechanics with minimal patient effort and operational ease. IOS applies pressure oscillations to the airways and analyzes the resulting signals. However, previous studies on IOS have been limited to frequency-domain features derived from its response signals, while neglecting valuable time-domain information. To bridge this gap, we developed a deep learning model that fuses time- and frequency-domain IOS data for lung function evaluation. An internal dataset (2,702 cases) and an external dataset (335 cases) were retrospectively collected for model training and validation. Model performance was first evaluated through ablation studies and then tested across different demographic subgroups. Finally, Grad-CAM was employed to improve model interpretability. Results showed that our model accurately predicted lung function parameters, including FEV₁/FVC (mean absolute errors [MAEs] of 3.78 and 4.33 %), FEV₁ (MAEs of 0.235 and 0.270 L), and FVC (MAEs of 0.264 and 0.315 L), in internal and external validation sets. The model also demonstrated strong performance in respiratory disease prescreening, achieving AUCs of 0.989 and 0.980 with sensitivities of 73.97 % and 71.47 % for detecting airway obstruction, and AUCs of 0.938 and 0.925 with sensitivities of 76.41 % and 66.24 % for classifying four ventilation patterns across the two sets. By fusing time- and frequency-domain IOS data, this study offers a new strategy for pulmonary function evaluation, facilitating more efficient prescreening for pulmonary diseases.

目前评估肺功能的方法需要大量的患者配合和严格的质量控制。相比之下，脉冲振荡测量法（IOS）是一种很有前途的替代方法，它可以以最小的患者努力和操作简便来测量肺部力学。IOS对气道施加压力振荡，并分析产生的信号。然而，以往对IOS的研究仅限于从其响应信号中提取的频域特征，而忽略了宝贵的时域信息。为了弥补这一差距，我们开发了一种深度学习模型，该模型融合了用于肺功能评估的时域和频域IOS数据。回顾性收集内部数据集（2702例）和外部数据集（335例）进行模型训练和验证。首先通过消融研究评估模型的性能，然后在不同的人口亚组中进行测试。最后，采用Grad-CAM提高模型的可解释性。结果表明，该模型能够准确预测肺功能参数，包括FEV1/FVC（平均绝对误差[MAEs]分别为3.78和4.33%）、FEV1（平均绝对误差[MAEs]分别为0.235和0.270 L）和FVC（平均绝对误差[MAEs]分别为0.264和0.315 L）。该模型在呼吸道疾病的预筛查中也表现出较强的性能，对气道阻塞的检测auc分别为0.989和0.980，灵敏度分别为73.97%和71.47%；对两组四种通气方式的分类auc分别为0.938和0.925，灵敏度分别为76.41%和66.24%。通过融合时频域IOS数据，本研究为肺功能评估提供了一种新的策略，有助于更有效地进行肺部疾病的预筛查。

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引用次数: 0

OMD: optimal transport-guided multimodal disentangled learning for leptomeningeal metastasis diagnosis OMD：用于小脑膜转移诊断的最佳转运引导的多模态解纠缠学习

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-11 DOI: 10.1016/j.inffus.2025.104121

Shengjia Chen , Huihua Hu , Hongfu Zeng , Chenxin Li , Qing Xu , Longfeng Zhang , Haipeng Xu

Leptomeningeal metastasis (LM) diagnosis represents a significant clinical challenge. Existing diagnostic approaches are often limited by their reliance on single-modality data and the inherent difficulties in effectively integrating heterogeneous information from imaging and genomics. To address these challenges, we propose OMD, an Optimal Transport-guided Multimodal Disentangled Learning framework that integrates MRI data with genomic information for enhanced diagnostic accuracy. Our method combines optimal transport-based cross-modal attention to robustly align heterogeneous features, information bottleneck compression to mitigate noise and redundancy, and feature disentanglement to explicitly model shared and modality-specific representations, integrated with hierarchical attention for MRI processing and graph-based cross-modal reasoning. Experimental results show that OMD achieves superior diagnostic accuracy, sensitivity, and specificity on our clinical dataset, substantially outperforming current state-of-the-art methods across all evaluation metrics. The model also provides interpretable insights into the cross-modal biomarkers associated with LM. The proposed OMD framework establishes a new paradigm for multimodal medical diagnosis that effectively addresses the complementary strengths of imaging and genomic data. Beyond its immediate application to LM diagnosis, our approach offers a generalizable methodology for integrating heterogeneous medical data sources while providing clinically relevant interpretability. This work represents an important step toward personalized medicine approaches that combine multiple data modalities for improved diagnostic accuracy and treatment planning.

轻脑膜转移（LM）的诊断是一个重大的临床挑战。现有的诊断方法往往受限于它们对单模态数据的依赖，以及在有效整合来自成像和基因组学的异构信息方面的固有困难。为了应对这些挑战，我们提出了OMD，这是一个优化运输引导的多模态解纠缠学习框架，它将MRI数据与基因组信息集成在一起，以提高诊断准确性。我们的方法结合了基于最佳传输的跨模态关注来稳健地对齐异构特征，信息瓶颈压缩来减轻噪声和冗余，特征解纠缠来明确地建模共享和特定模态表示，结合了MRI处理的分层关注和基于图的跨模态推理。实验结果表明，在我们的临床数据集上，OMD实现了卓越的诊断准确性、敏感性和特异性，在所有评估指标上都大大优于当前最先进的方法。该模型还为与LM相关的跨模态生物标志物提供了可解释的见解。拟议的OMD框架为多模式医学诊断建立了一个新的范例，有效地解决了成像和基因组数据的互补优势。除了直接应用于LM诊断之外，我们的方法还提供了一种可推广的方法，用于整合异构医疗数据源，同时提供临床相关的可解释性。这项工作代表了个性化医疗方法的重要一步，将多种数据模式结合起来，以提高诊断准确性和治疗计划。

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引用次数: 0

MCIVA: A multi-view pedestrian detection framework with a central inverse nearest neighbor map and a view adaptive module 基于中心逆最近邻映射和视图自适应模块的多视图行人检测框架

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-11 DOI: 10.1016/j.inffus.2026.104142

He Li , Taiyu Liao , Weihang Kong , Xingchen Zhang

Multi-view pedestrian detection is an important task and has many applications in areas such as surveillance and smart cities. Despite the significant performance improvements achieved in recent multi-view pedestrian detection methods, there are still three main challenges for this task. First, in crowded areas, neighboring connected components may merge in dense regions, resulting in unclear localization of pixel peaks for each pedestrian. Second, the loss functions used in previous multi-view pedestrian detection methods have a high response to background regions. Third, camera parameters have not been fully utilized; they are only used to generate fixed projection matrices. To address these challenges, we propose a novel multi-view pedestrian detection framework (MCIVA) with a central inverse nearest neighbor (CINN) map and a view adaptive module (VAM). The CINN map is introduced to generate the ground-truth probability occupancy map (POM) based on annotations, providing more precise location information for each pedestrian. To enhance the model’s attention to local structural information, we propose a local structural similarity loss to reduce the influence of false local maxima in background regions. Moreover, the VAM is introduced to utilize camera parameters to generate learnable weights for multi-view feature fusion. We evaluate the proposed method on three benchmark datasets, and the results show that the proposed MCIVA improves the quality of prediction maps and achieves state-of-the-art performance.

多视角行人检测是一项重要的任务，在监控和智慧城市等领域有着广泛的应用。尽管最近的多视角行人检测方法取得了显著的性能进步，但这项任务仍然存在三个主要挑战。1)在拥挤区域，相邻的连通组件可能在密集区域合并，导致每个行人像素峰值定位不清。2)以往多视角行人检测方法中使用的损失函数对背景的响应较高。3)相机参数没有被充分利用；它们仅用于生成定值投影矩阵。为了解决这些挑战，我们提出了一种新的多视图行人检测框架，该框架具有中心逆最近邻地图和视图自适应模块（MCIVA）。引入中心逆近邻图（Central Inverse Nearest Neighbor， CINN）生成基于标注的地真概率占用图（ground-truth Probability Occupancy map， POM），为每个行人提供更精确的位置信息。为了增强模型对局部结构信息的关注，我们提出了局部结构相似度损失来减少背景区域虚假局部极大值的影响。此外，引入了一种新型的即插即用视图自适应模块（VAM），利用摄像机参数生成可学习的权重，用于多视图特征融合。我们在三个基准数据集上对所提出的方法进行了评估，结果表明所提出的MCIVA方法显著提高了预测图的质量，达到了最先进的性能。

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引用次数: 0

Secure Tobit filtering for multi-rate nonlinear systems under multi-node random access protocol: A Paillier encryption-decryption mechanism 多节点随机访问协议下多速率非线性系统的安全Tobit滤波：一种Paillier加解密机制

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-10 DOI: 10.1016/j.inffus.2026.104146

Shuo Yang , Raquel Caballero-Águila , Jun Hu , Antonia Oya-Lechuga

In this paper, the secure Tobit filtering (TF) problem is investigated for nonlinear systems subject to measurement censoring under a multi-node random access protocol (MNRAP). A multi-rate sampling framework is considered, which allows the system states and measurement outputs to operate with distinct sampling periods, thus reflecting practical engineering constraints. Furthermore, to mitigate data collisions and improve resource utilization, the MNRAP is adopted to regulate the transmission order of measurement signals over communication networks. In addition, to safeguard the communication confidentiality between the sensor node and the filter, the Paillier encryption-decryption mechanism is incorporated. This protects the transmitted information from being intercepted by unauthorized third parties. This paper concentrates on developing an innovative secure TF scheme that guarantees the existence of an upper bound (UB) on the filtering error second moment. Subsequently, the minimization of the obtained UB is carried out in the trace sense by designing a proper filter gain. Additionally, the uniform boundedness of the filtering error is verified in the mean-square sense by establishing a sufficient criterion. Finally, the efficacy and advantages of the proposed secure TF approach are demonstrated through a simulation example.

研究了在多节点随机访问协议（MNRAP）下，受测量滤波约束的非线性系统的安全Tobit滤波问题。考虑了一个多速率采样框架，它允许系统状态和测量输出在不同的采样周期下运行，从而反映了实际的工程约束。此外，为了减少数据冲突，提高资源利用率，采用MNRAP规范通信网络中测量信号的传输顺序。此外，为了保证传感器节点与滤波器之间通信的机密性，还引入了Paillier加解密机制。这样可以防止传输的信息被未经授权的第三方截获。本文研究了一种保证滤波误差秒矩存在上界的安全TF方案。随后，通过设计适当的滤波器增益，在迹线意义上实现了所得UB的最小化。此外，通过建立一个充分的判据，在均方意义上验证了滤波误差的均匀有界性。最后，通过仿真实例验证了该方法的有效性和优越性。

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引用次数: 0

Team collaboration-oriented multi-agent pathfinding and probabilistic verification 面向团队协作的多智能体寻路与概率验证

IF 15.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2026-01-10 DOI: 10.1016/j.inffus.2026.104125

Xia Wang , Jun Liu , Chris D. Nugent , Shaobing Xu , Guanfeng Wu

Multi-agent pathfinding and its reliable execution in stochastic environments represent a critical challenge for real-world applications, demanding both the planning of efficient paths and the formal assurance of safe, conflict-free operation. This paper introduces a novel methodology framework to address this dual requirement. To maximize operational efficiency, we introduce a strategy for optimal goal allocation for team collaboration, integrating it with the conflict-based search algorithm to minimize the total move counts required for mission completion. The second component is an integrated verification process grounded in probabilistic model checking. We model the multi-agent path execution process under stochastic uncertainties using a Markov decision process. By leveraging the probabilistic model checker and probabilistic computation tree logic, the framework formally verifies critical safety properties, ensuring conflict-free and deadlock-free path execution. Furthermore, it evaluates the effectiveness of proposed behavioral constraints designed to mitigate stochastic delays, thereby verifying the overall system safety. By fusing multi-agent planning, probabilistic reasoning, and formal logic-based verification, the proposed framework establishes a foundation amenable to natural extension for addressing multi-agent decision-making and uncertainty estimation. Case study results demonstrate that our methodology effectively selects the pathfinding solution with the minimum move count while significantly enhancing overall system safety through these formally verified behavioral constraints.

多智能体寻路及其在随机环境中的可靠执行对现实世界的应用来说是一个关键的挑战，它既要求规划有效的路径，又要求正式保证安全、无冲突的操作。本文介绍了一种新的方法框架来解决这一双重要求。为了使操作效率最大化，我们引入了一种团队协作的最佳目标分配策略，将其与基于冲突的搜索算法相结合，以最小化完成任务所需的总移动次数。第二个组成部分是基于概率模型检查的集成验证过程。利用马尔可夫决策过程对随机不确定性下的多智能体路径执行过程进行建模。通过利用概率模型检查器和概率计算树逻辑，框架正式验证关键安全属性，确保无冲突和无死锁的路径执行。此外，它还评估了旨在减轻随机延迟的行为约束的有效性，从而验证了整个系统的安全性。通过融合多智能体规划、概率推理和基于形式逻辑的验证，该框架为解决多智能体决策和不确定性估计问题建立了一个可自然扩展的基础。案例研究结果表明，我们的方法有效地选择了移动次数最少的寻路解决方案，同时通过这些正式验证的行为约束显著提高了整体系统的安全性。

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引用次数: 0