[1] GAO H, CHENG B, WANG J, et al. Object classification using CNN-based fusion of vision and LIDAR in au-tonomous vehicle environment[J]. IEEE Transactions on Industrial Informatics, 2018, 14(9): 4224-4231.
[2] YEONG D J, VELASCO-HERNANDEZ G, BARRY J, et al. Sensor and sensor fusion technology in autonomous vehicles: A review[J]. Sensors, 2021, 21(6): 2140.
[3] WANG Q, TIAN X, LI D. Multimodal soft jumping robot with self-decision ability[J]. Smart Materials and Struc-tures, 2021, 30(8): 085038.
[4] HRAMOV A E, MAKSIMENKO V A, PISARCHIK A N. Physical principles of brain-computer interfaces and their applications for rehabilitation, robotics and con-trol of human brain states[J]. Physics Reports, 2021, 918: 1-133.
[5] NASEER N, HONG K S. fNIRS-based brain-computer interfaces: A review[J]. Frontiers in human neurosci-ence, 2015, 9: Article 3.
[6] ABIRI R, BORHANI S, SELLERS E W, et al. A compre-hensive review of EEG-based brain-computer interface paradigms[J]. Journal of neural engineering, 2019, 16(1): 011001.
[7] 朱军. 会思考的硬核“椰城”：城市大脑塑造未来科幻城市[J].互联网经济, 2020(Z2): 68-71.
ZHU J. The thinking hardcore “Coconut City”: Urban brain shapes future sci fi cities[J]. Internet economy, 2020(Z2): 68-71 (in Chinese).
[8] 周光霞. 美军联合信息环境建设情况分析及启示[J]. 指挥与控制学报, 2016, 2(4): 354-360.
ZHOU G X. Analysis and insights on the construction of joint information environment in the US military[J]. Journal of Command and Control, 2016, 2(4): 354-360 (in Chinese).
[9] OpenAI. Video generation models as world simulators [EB/OL]. https://openai.com/research/video-generation-models-as-world-simulators. (2024-02-16).
[10] 何友, 王国宏, 陆大?, 等. 多传感器信息融合及应用[M]. 北京: 电子工业出版社, 2000.
HE Y, WANG G H, LU D J, et al. Multisensor Infor-mation Fusion With Applications[M]. Beijing: Publish-ing House of Electronics Industry, 2000 (in Chinese).
[11] CIUONZO D, ROSSI P S, VARSHNEY P K. Distributed detection in wireless sensor networks under multiplica-tive fading via generalized score tests[J]. IEEE Internet of Things Journal, 2021, 8(11): 9059-9071.
[12] LI C, LI G, VARSHNEY P K. Distributed detection of sparse signals with censoring sensors in clustered sen-sor networks[J]. Information Fusion, 2022, 83: 1-18.
[13] WANG X, LI G, QUAN C, et al. Distributed detection of sparse stochastic signals with quantized measurements: The generalized Gaussian case[J]. IEEE Transactions on Signal Processing, 2019, 67(18): 4886-4898.
[14] VARSHNEY P K. Distributed detection and data fu-sion[M]. Springer Science and Business Media, 2012.
[15] LI C, LI G, VARSHNEY P K. Distributed detection of sparse signals with censoring sensors in clustered sen-sor networks[J]. Information Fusion, 2022, 83: 1-18.
[16] ANDREA A, BARNI M, KALLAS K, et al. Information Fusion in Distributed Sensor Networks With Byzan-tines[M]. Springer, 2021.
[17] KAYAALP M, BORDIGNON V, SAYED A H. Social opinion formation and decision making under commu-nication trends[J]. IEEE Transactions on Signal Pro-cessing, 2024, 72: 506-520.
[18] VLASKI S, KAR S, SAYED A H, et al. Networked sig-nal and information processing: Learning by multiagent systems[J]. IEEE Signal Processing Magazine, 2023, 40(5): 92-105.
[19] QUAN C, SRIRANGA N, YANG H, et al. Efficient or-dered-transmission based distributed detection under data falsification attacks[J]. IEEE Signal Processing Letters, 2023, 30: 145-149.
[20] TORRA V, NARUKAWA Y. Modeling Decisions: Infor-mation Fusion and Aggregation Operators[M]. Springer Science and Business Media, 2007.
[21] HALL D, CHONG C-Y, LLINAS J, et al. Distribut-ed Data Fusion for Network-Centric Operations[M]. Boca Raton, FL: CRC Press, 2013.
[22] QIN Z, ZHAO P, ZHUANG T, et al. A survey of identity recognition via data fusion and feature learning[J]. In-formation Fusion, 2023, 91: 694-712.
[23] 徐从富, 耿卫东, 潘云鹤. 面向数据融合的DS方法综述[J]. 电子学报, 2001(03): 393-396.
XU C F, GENG W D, PAN Y H. A review of DS meth-ods for data fusion [J]. Acta Electronica Sinica, 2001(03): 393-396 (In Chinese).
[24] ZAHO J, DENG Y. Complex network modeling of evi-dence theory [J]. IEEE Transactions on Fuzzy Systems, 2020, 29(11): 3470-3480.
[25] LIU Z, DEZERT J, MERCIER G, et al. Dynamic evi-dential reasoning for change detection in remote sens-ing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(5): 1955-1967.
[26] DONG Y, LI X, DEZERT J, et al. Dezert-Smarandache theory-based fusion for human activity recognition in body sensor networks[J]. IEEE Transactions on Indus-trial Informatics, 2020, 16(11): 7138-7149.
[27] 韩崇昭, 朱洪艳, 段战胜. 多源信息融合（第3版）[M]. 北京: 清华大学出版社, 2022.
HAN C Z, ZHU H Y, DUAN Z S. Multisource Infor-mation Fusion (3rd Edition) [M]. Beijing: Tsinghua University Press, 2022 (in Chinese).
[28] Zhu Y, You Z, Zhao J, Zhang K, Li X R. The optimality for the distributed Kalman filtering fusion with feed-back[J]. Automatica, 2001, 37(9): 1489-1493.
[29] JULIER S J, UHLMANN J K. A non-divergent estima-tion algorithm in the presence of unknown correla-tions[C]. In Proceedings of the 1997 American Control Conference, 1997: 2369-2373.
[30] FARRELL W J, GANESH C. Generalized Chernoff fusion approximation for practical distributed data fu-sion[C]. In Proceedings of 2009 12th International Con-ference on Information Fusion, 2009: 555-562.
[31] REINHARDT M, NOACK B, ARAMBEL P O, et al. Minimum covariance bounds for the fusion under un-known correlations[J]. IEEE Signal Processing Letters, 2015, 22(9): 1210-1214.
[32] SIJS J, LAZAR M. State fusion with unknown correla-tion: Ellipsoidal intersection[J]. Automatica, 2012, 48(8): 1874-1878.
[33] NOACK B, SIJS J, REINHARDT M, et al. Decentral-ized data fusion with inverse covariance intersection[J]. Automatica, 2017, 79: 35-41.
[34] REECE S, ROBERTS S. Generalised covariance union: A unified approach to hypothesis merging in tracking[J]. IEEE Transactions on Aerospace and Electronic Sys-tems, 2010, 46(1): 207-221.
[35] SHAFIEEZADEHABADEH S, NGUYEN V A, KUHN D, et al. Wasserstein distributionally robust Kalman filtering[J]. Advances in Neural Information Processing Systems, 2018, 31: 1-10.
[36] WANG S, WU Z, LIM A. Robust state estimation for linear systems under distributional Uncertainty[J]. IEEE Transactions on Signal Processing, 2021, 69: 5963-5978.
[37] ZORZI M. Distributed Kalman Filtering Under Model Uncertainty[J]. IEEE Transactions on Control of Net-work Systems, 2020, 7(2): 990-1001.
[38] YU X, LI J. Distributed robust Kalman filters under model uncertainty and multiplicative disturbance[J]. IEEE Transactions on Aerospace and Electronic Sys-tems, 2023, 59(2): 973-988.
[39] NIU D, SONG E, LI Z, et al. A marginal distributionally robust MMSE estimation for a multisensor system with Kullback-Leibler divergence constraints[J]. IEEE Transactions on Signal Processing, 2023, 71: 3772-3787.
[40] AL HAGE J, E. EL NAJJAR M, POMORSKI D. Multi-sensor fusion approach with fault detection and exclu-sion based on the Kullback–Leibler divergence: Appli-cation on collaborative multi-robot system[J]. Infor-mation Fusion, 2017, 37: 61-76.
[41] BAR-SHALOM Y. Update with out-of-sequence meas-urements in tracking: Exact solution[J]. IEEE Transac-tions on Aerospace and Electronic Systems, 2002, 38(3): 769-777.
[42] BAR-SHALOM Y, HUMIN C, MALICK M. One-step solution for the multistep out-of-sequence-measurement problem in tracking[J]. IEEE Transac-tions on Aerospace and Electronic Systems, 2004, 40(1): 27-37.
[43] GOVAERS F, KOCH W. Generalized solution to smoothing and out-of-sequence processing[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(3): 1739-1748.
[44] KIM Y, HONG K, BANG H. Utilizing out-of-sequence measurement for ambiguous update in particle filter-ing[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(1): 493-501.
[45] ZHANG S, BAR-SHOM Y. Optimal update with multi-ple out-of-sequence measurements with arbitrary arriv-ing order[J]. IEEE Transactions on Aerospace and Elec-tronic Systems, 2012, 48(4): 3116-3132.
[46] GARCIA-FERNANDEZ A F, YI W. Continuous-discrete multiple target tracking with out-of-sequence meas-urements[J]. IEEE Transactions on Signal Processing, 2021, 69: 4699-4709.
[47] MARELLI D, SUI T, FU M. Distributed Kalman estima-tion with decoupled local filters[J]. Automatica, 2021, 130: 109724.
[48] BATTISTELLI G, CHISCI L. Kullback–Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability[J]. Automatica, 2014, 50(3): 707-718.
[49] BATTISTELLI G, CHISCI L, MUGNAI G, et al. Con-sensus-based linear and nonlinear filtering[J]. IEEE Transactions on Automatic Control, 2015, 60(5): 1410-1415.
[50] 金浩. 多传感器网络化系统的分布式估计算法研究[D]. 黑龙江大学, 2022.
JIN H. Research on distributed estimation algorithms for multi-sensor networked systems [D]. Heilongjiang University, 2022 (in Chinese).
[51] LI Y, QUEVEDO D E, LAU V, et al. Optimal periodic transmission power schedules for remote estimation of ARMA processes[J]. IEEE Transactions on Signal Pro-cessing, 2013, 61(24): 6164-6174.
[52] DEKKERS G, ROSAS F, VAN WATERSCHOOT T, et al. Dynamic sensor activation and decision-level fusion in wireless acoustic sensor networks for classification of domestic activities[J]. Information Fusion, 2022, 77: 196-210.
[53] ZHOU J, GU G, CHEN X. Distributed Kalman filtering over wireless sensor networks in the presence of data packet drops[J]. IEEE Transactions on Automatic Con-trol, 2019, 64(4): 1603-1610.
[54] YANG H, LI H, XIA Y, et al. Distributed Kalman filter-ing over sensor networks with transmission delays[J]. IEEE Transactions on Cybernetics, 2021, 51(11): 5511-5521.
[55] LIU H, NIU B, LI Y. False-data-injection attacks on remote distributed consensus estimation[J]. IEEE Transactions on Cybernetics, 2022, 52(1): 433-443.
[56] YANG W, ZHANG Y, CHEN G, et al. Distributed filter-ing under false data injection attacks[J]. Automatica, 2019, 102: 34-44.
[57] LI L, YANG H, XIA Y, et al. Event-based distributed state estimation for linear systems under unknown input and false data injection attack[J]. Signal Processing, 2020, 170: 107423.
[58] LYNEN S, ACHTELIK M W, WEISS S, et al. A robust and modular multi-sensor fusion approach applied to MAV navigation[C]. In Proceedings of 2013 IEEE/RSJ International Conference on Intelligent Robots and Sys-tems, 2013: 3923-3929.
[59] TANG X, ZHANG Z, QIN Y. On-road object detection and tracking based on radar and vision fusion: A re-view[J]. IEEE Intelligent Transportation Systems Mag-azine, 2022, 14(5): 103-128.
[60] MAHFOUZ S, MOURAD-CHEHADE F, et al. Target tracking using machine learning and Kalman filter in wireless sensor networks[J]. IEEE Sensors Journal, 2014, 14(10): 3715-3725.
[61] ZHANG Y, SONG B, DU X, et al. Vehicle Tracking Using surveillance with multimodal data fusion[J]. IEEE Transactions on Intelligent Transportation Sys-tems, 2018, 19(7): 2353-2361.
[62] CAO J, ZHANG H, JIN L, et al. A review of object tracking methods: From general field to autonomous vehicles[J]. Neurocomputing, 2024, 585: 127635.
[63] YEOM S-W, KIRUBARAJAN T, BAR-SHALOM Y. Track segment association, fine-step IMM and initiali-zation with Doppler for improved track performance[J]. IEEE Transactions on aerospace and electronic systems, 2004, 40(1): 293-309.
[64] 杜渐, 夏学知. 面向航迹中断的模糊航迹关联算法[J].火力与指挥控制, 2013, 38(6):68-7176.
DU J, XIA X Z, A fuzzy track association algorithm in track interrupt-oriented[J]. Fire Control and Command Control,2013,38(6):68-7176 (in Chinese).
[65] 何友, 彭应宁, 陆大?, 等. 分布式多传感器数据融合中的双门限航迹相关算法[J]. 电子与信息学报, 1997, 19(6): 721-728.
HE Y, PENG Y, LU D, et al. Binary track correlation algorithms in a distributed multi-sensor data fusion sys-tem [J]. Journal of Electronics and Information Tech-nology, 1997, 19(6): 721-728 (in Chinese).
[66] 何友, 彭应宁. 基于模糊综合函数的航迹关联算法[J]. 电子与信息学报, 1999, 021(001): 91-96.
HE Y, PENG Y N. Track association algorithm based on fuzzy comprehensive function [J]. Journal of Electron-ics and Information Technology, 1999, 021(001): 91-96 (in Chinese).
[67] 徐毓, 金以慧. 基于多尺度小波变换和短时分形理论的航迹关联方法[J]. 控制与决策, 2003, 18(4): 5.
XU Y, JIN Y H. Track association method based on multi-scale wavelet transform and short-time fractal theory [J]. Control and Decision, 2003, 18(4): 5 (in Chinese).
[68] TIAN W, WANG Y, SHAN X, et al. Track-to-track asso-ciation for biased data based on the reference topology feature[J]. IEEE Signal Processing Letters,2014, 21 (4): 449-453.
[69] 何友, 宋强, 熊伟. 基于相位相关的航迹对准关联技术[J]. 电子学报, 2010, 38(12): 6.
HE Y, SONG Q, XIONG W. A track correlation algo-rithm based on new space-time cross-point feature [J]. Acta Electronica Sinica, 2010, 38(12): 6 (in Chinese).
[70] XIONG W, XU P, CUI Y, et al. Track segment associa-tion with dual contrast neural network[J]. IEEE Trans-actions on Aerospace and Electronic Systems, 2021, 58(1): 247-261.
[71] XIONG W, XU P L, CUI Y Q. HTG-TA: Heterogenous track graph for asynchronous track-to-track associa-tion[J]. IEEE Transactions on Aerospace and Electronic Systems, 2024, 60(5): 7232-7250.
[72] JIN B, TANG Y, ZHANG Z, et al. Radar and AIS track association integrated track and scene features through deep learning[J]. IEEE Sensors Journal, 2023, 23(7): 8001-8009.
[73] APTOULA E. Remote sensing image retrieval with global morphological texture descriptors[J/OL]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(5): 3023-3034.
[74] XIE J, FANG Y, ZHU F, et al. Deepshape: Deep learned shape descriptor for 3D shape matching and retriev-al[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017: 1335-1345,
[75] LI P, REN P, ZHANG X, et al. Region-wise deep feature representation for remote sensing images[J]. Remote Sensing, 2018, 10(6): 871.
[76] XIONG W, XIONG Z, CUI Y, et al. A discriminative distillation network for cross-source remote sensing image retrieval[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 99: 1234-1247.
[77] CHEN H, DING G, LIU X, et al. IMRAM: Iterative matching with recurrent attention memory for cross-modal image-Text Retrieval[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020:12652-12660.
[78] CHI P, FENG Y, ZHOU M, et al. TIAR: Text-Image-Audio Retrieval with weighted multimodal re-ranking[J]. Applied Intelligence, 2023, 53(19): 22898-22916.
[79] ARAUJO A, GIROD B. Large-scale video retrieval using image queries[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(6): 1406-1420
[80] HOTELLING H. Relations between two sets of vari-ates[M]. Breakthroughs in Statistics: Methodology and Distribution. New York, NY: Springer New York, 1992.
[81] LV Y, XIONG W, ZHANG X, et al. Fusion-based correlation learning model for cross-modal remote sensing image retrieval[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19(1): 1-5.
[82] 李浩然, 熊伟, 崔亚奇, 等. 相似度矩阵辅助遥感图像无监督哈希跨模态关联 [J]. 光子学报, 2023, 52(01): 216-227.
Li H R, XIONG W, CUI Y Q, et al. Enhancing remote sensing image unsupervised hashing cross-modal corre-lation with similarity matrix [J]. Acta Photonica Sinica, 2023, 52(01): 216-227 (in Chinese).
[83] FAN L, ZHANG F, FAN H, et al. Brief review of image denoising techniques[J]. Visual Computing for Industry, Biomedicine, and Art, 2019, 2(1): 7.
[84] HASKINS G, KRUGER U, YAN P. Deep learning in medical image registration: a survey[J]. Machine Vi-sion and Applications, 2020, 31(1): 1-18.
[85] LI G. Advanced Sparsity-Driven Models and Methods for Radar Applications[M]. SciTech/IET Publishing, 2020.
[86] WRIGHT J, YANG A Y, GANESH A, et al. Robust face recognition via sparse representation[J]. IEEE Transac-tions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227.
[87] GKILLAS A, AMPELIOTIS D, BERBERIDIS K. Con-nections between deep equilibrium and sparse represen-tation models with application to hyperspectral image denoising[J]. IEEE Transactions on Image Processing, 2023, 32: 1513-1528.
[88] AHARON M, ELAD M, BRUCKSTEIN A. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322.
[89] SCETBON M, ELAD M, MILANFAR P. Deep K-SVD Denoising[J]. IEEE Transactions on Image Pro-cessing, 2021, 30: 5944-5955.
[90] LI H, WU X. DenseFuse: A fusion approach to infrared and visible images[J]. IEEE Transactions on Image Processing, 2019, 28(5): 2614-2623.
[91] ZHAO F, ZHAO W, YAO L, et al. Self-supervised fea-ture adaption for infrared and visible image fusion[J]. Information Fusion, 2021, 76: 189-203.
[92] LIU J, WU Y, HUANG Z, et al. SMoA: Searching a modality-oriented architecture for infrared and visible image fusion[J]. IEEE Signal Processing Letters, 2021, 28: 1818-1822.
[93] XU H, WANG X, MA J. DRF: Disentangled representa-tion for visible and infrared image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-13.
[94] TANG L, YUAN J, MA J. Image fusion in the loop of high-level vision tasks: A semantic-aware real-time in-frared and visible image fusion network[J]. Information Fusion, 2022, 82: 28-42.
[95] MA J, YU W, LIANG P, et al. FusionGAN: A generative adversarial network for infrared and visible image fu-sion[J]. Information Fusion, 2019, 48: 11-26.
[96] LI J, HUO H, LIU K, et al. Infrared and visible image fusion using dual discriminators generative adversarial networks with Wasserstein distance[J]. Information Sciences, 2020, 529: 28-41.
[97] GU Y, WANG X, ZHANG C, et al. Advanced driving assistance based on the fusion of infrared and visible images[J]. Entropy, 2021, 23(2): 239.
[98] WANG X, ZHU D, LI G, et al. Proposal-Copula-based fusion of spaceborne and airborne SAR images for ship target detection[J]. Information Fusion, 2022, 77: 247-260.
[99] LI W, WANG X, LI G, et al. NN-Copula-CD: A copula-guided interpretable neural network for change detec-tion in heterogeneous remote sensing images[J/OL]. arXiv preprint, 2023, arXiv:2303.17448[2023-03-30].
[100] FANG Q, WANG Z. Cross-modality attentive feature fusion for object detection in multispectral remote sensing imagery[J]. Pattern Recognition, 2022, 130: 108786.
[101] WANG Q, CHI Y, SHEN T, et al. Improving RGB-infrared object detection by reducing cross-modality redundancy[J]. Remote Sensing, 2022, 14(9): 2020.
[102] JIANG X, LI G, LIU Y, et al. Change detection in het-erogeneous optical and SAR remote sensing images via deep homogeneous feature fusion[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 1551-1566.
[103] MATASCI G, PLANTE J, KASA K, et al. Deep learn-ing for vessel detection and identification from space-borne optical imagery[J]. ISPRS Annals of the Photo-grammetry, Remote Sensing and Spatial Information Sciences, 2021, 3: 303-310.
[104] ZHANG L, LIU Y, WANG X, et al. GLRT-based met-ric learning for remote sensing object retrieval[J/OL]. arXiv preprint, 2024, arXiv:2410.05773[2024-10-20].
[105] XIAO G, BAVIRISETTI D P, LIU G, et al. Image Fu-sion[M]. Singapore: Springer, 2020.
[106] LIU C, YANG H, FU J, et al. Learning trajectory-aware transformer for video super-resolution[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 5687-5699.
[107] GAONKAR A, CHUKKAPALLI Y, RAMAN P J, et al. A comprehensive survey on multimodal data represen-tation and information fusion algorithms[C]. 2021 In-ternational Conference on Intelligent Technologies, 2021: 1-8.
[108] KLUPACS J, GOSTAR A K, RATHNAYAKE T, et al. Multiagent information fusion for connected driving: A review[J]. IEEE Access, 2022, 10: 85030-85049.
[109] LIN S, HAN Z, LI D, et al. Integrating model-and data-driven methods for synchronous adaptive multi-band image fusion[J]. Information Fusion, 2020, 54: 145-160.
[110] WEN J, JIANG D, TU G, et al. Dynamic interactive multiview memory network for emotion recognition in conversation[J]. Information Fusion, 2023, 91: 123-133.
[111] XU J , REN Y , SHI X ,et al. UNTIE: Clustering analy-sis with disentanglement in multi-view information fu-sion[J]. Information Fusion, 2023, 100:101937.
[112] NIE, FEIPING, et al. An effective and efficient algo-rithm for K-means clustering with new formulation[J]. IEEE Transactions on Knowledge and Data Engineer-ing, 2022 35(4) : 3433-3443.
[113] LIU T , ZHOU Z , YANG L .Layered isolation forest: A multi-level subspace algorithm for improving isolation forest[J].Neurocomputing, 2024, 581:127525.
[114] DU J , HAN G , LIN C ,et al. ITrust: An anomaly-resilient trust model based on isolation forest for un-derwater acoustic sensor networks[J]. IEEE Transac-tions on Mobile Computing, 2020, 21(5): 1684-1696.
[115] ZHANG, PEI, et al. Real-time malicious traffic detec-tion with online isolation forest over SD-WAN[J]. IEEE Transactions on Information Forensics and Security 18: 2076-2090.
[116] YANG C, LIU T, CHEN G, et al. ICSFF: Information constraint on self-supervised feature fusion for few-shot remote sensing image classification[J]. IEEE Transac-tions on Geoscience and Remote Sensing, 2024, 62: 1-12.
[117] ZHANG Y, LI W, ZHANG M, et al. Graph information aggregation cross-domain few-shot learning for hyper-spectral image classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 35(2): 1912-1925.
[118] WU L, LI Z, ZHAO H, et al. Recognizing unseen ob-jects via multimodal intensive knowledge graph propa-gation[C]. Proceedings of the 29th ACM SIGKDD Con-ference on Knowledge Discovery and Data Mining. 2023: 2618-2628.
[119] JIANG X , LI G , ZHANG X P ,et al. A semi-supervised Siamese network for efficient change detec-tion in heterogeneous remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-18.
[120] CHUN, DAYOUNG, SEUNGIL L, et al. USD: Uncer-tainty-based one-phase learning to enhance pseudo-label reliability for semi-supervised object detection[J]. IEEE Transactions on Multimedia, 2024, 26: 6336-6347.
[121] ZHOU Z, ZHAO L, JI K, et al. A domain adaptive few-shot SAR ship detection algorithm driven by the latent similarity between optical and SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 1-18.
[122] HU, HE-XUAN, et al. Multi-source information fusion based DLaaS for traffic flow prediction[J]. IEEE Transactions on Computers, 2024, 73(4): 994-1003.
[123] GUO Y, LIU R W, QU J, et al. Asynchronous trajectory matching-based multimodal maritime data fusion for vessel traffic surveillance in inland waterways[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(11): 12779-12792.
[124] LIU Y, LIU J, YANG K, et al. Amp-net: Appearance-motion prototype network assisted automatic video anomaly detection system[J]. IEEE Transactions on In-dustrial Informatics, 2023, 20(2): 1-13.
[125] 李伯虎. 云制造系统3.0: 一种适应新时代、新态势、新征程的先进智能制造系统[J].电气时代, 2022, (1): 18-19.
LI B H. Cloud manufacturing system3.0: An advanced intelligent manufacturing system adapted to the new era, New Situation, and New Journey[J]. Electrical Age, 2022, (1): 18-19 (in Chinese).
[126] MINNETT P J, ALVERA-AZCARATE A, CHIN T M, et al. Half a century of satellite remote sensing of sea-surface temperature[J]. Remote sensing of Environment, 2019, 233(11): 66.
[127] YANG G, YE Q, XIA J. Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond[J]. Information Fusion, 2022, 77: 29-52.
[128] UDDIN M Z, HASSAN M M, ALSANAD A, et al. A body sensor data fusion and deep recurrent neural net-work-based behavior recognition approach for robust healthcare[J]. Information Fusion, 2020, 55: 105-115.
[129] ZHANG Y D, DONG Z, WANG S H, et al. Advances in multimodal data fusion in neuroimaging: overview, challenges, and novel orientation[J]. Information Fu-sion, 2020, 64: 149-187.
[130] JI B, ZHANG X, MUMTAZ S, et al. Survey on the internet of vehicles: Network architectures and applica-tions[J]. IEEE Communications Standards Magazine, 2020, 4(1): 34-41.
[131] 汲克山, 刘思培, 李清玉, 等.大模型在军事领域的应用与展望[C]. 第十二届中国指挥控制大会, 2024: 79-83.
JI K S, LIU S P, LI Q Y, et al. The Application and Pro-spect of Large Models in the Military Field[C]. The 12th China Command and Control Conference, 2024: 79-83 (in Chinese).
CJA