Space-Based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques
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A. Farina | P. Willett | A. D. Simone | P. Braca | A. Iodice | D. Riccio | D. Gaglione | S. Carniel | N. Forti | L. Millefiori | Giovanni Soldi | F. Daffinà | Gianfausto Bottini | Dino Quattrociocchi
[1] A. Farina,et al. Space-Based Global Maritime Surveillance. Part I: Satellite Technologies , 2020, IEEE Aerospace and Electronic Systems Magazine.
[2] Moe Z. Win,et al. Bayesian information fusion and multitarget tracking for maritime situational awareness , 2020, IET Radar, Sonar & Navigation.
[3] Paolo Braca,et al. Random Finite Set Tracking for Anomaly Detection in the Presence of Clutter , 2020, 2020 IEEE Radar Conference (RadarConf20).
[4] F. Hlawatsch,et al. Classification-Aided Multitarget Tracking Using the Sum-Product Algorithm , 2020, IEEE Signal Processing Letters.
[5] Giovanni De Magistris,et al. Underwater Tracking Based on the Sum-Product Algorithm Enhanced by a Neural Network Detections Classifier , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[6] Liye Zhang,et al. Traffic Pattern Mining and Forecasting Technologies in Maritime Traffic Service Networks: A Comprehensive Survey , 2020, IEEE Transactions on Intelligent Transportation Systems.
[7] Paolo Braca,et al. Prediction oof Vessel Trajectories From AIS Data Via Sequence-To-Sequence Recurrent Neural Networks , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[8] Marios Vodas,et al. A Distributed Spatial Method for Modeling Maritime Routes , 2020, IEEE Access.
[9] Antonio Iodice,et al. Semantic Segmentation using Deep Learning: A case of study in Albufera Park, Valencia , 2019, 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor).
[10] Paolo Braca,et al. Self-Tuning Algorithms for Multisensor-Multitarget Tracking Using Belief Propagation , 2019, IEEE Transactions on Signal Processing.
[11] Paolo Braca,et al. Unsupervised extraction of maritime patterns of life from Automatic Identification System data , 2019, OCEANS 2019 - Marseille.
[12] Peter Willett,et al. Anomaly Detection and Tracking Based on Mean–Reverting Processes with Unknown Parameters , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[13] Moe Z. Win,et al. Heterogeneous Information Fusion for Multitarget Tracking Using the Sum-product Algorithm , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[14] Paolo Braca,et al. Detecting Anomalous Deviations From Standard Maritime Routes Using the Ornstein–Uhlenbeck Process , 2018, IEEE Transactions on Signal Processing.
[15] Paolo Braca,et al. Multiple Ornstein–Uhlenbeck Processes for Maritime Traffic Graph Representation , 2018, IEEE Transactions on Aerospace and Electronic Systems.
[16] Simone Marinai,et al. Historical Handwritten Document Segmentation by Using a Weighted Loss , 2018, ANNPR.
[17] Paolo Braca,et al. Maritime Anomaly Detection Based on Mean-Reverting Stochastic Processes Applied to a Real-World Scenario , 2018, 2018 21st International Conference on Information Fusion (FUSION).
[18] Paolo Braca,et al. Belief Propagation Based AIS/Radar Data Fusion for Multi - Target Tracking , 2018, 2018 21st International Conference on Information Fusion (FUSION).
[19] Nicola Forti,et al. Hybrid Bernoulli Filtering for Detection and Tracking of Anomalous Path Deviations , 2018, 2018 21st International Conference on Information Fusion (FUSION).
[20] Paolo Braca,et al. Online Estimation of Unknown Parameters in Multisensor-Multitarget Tracking: a Belief Propagation Approach , 2018, 2018 21st International Conference on Information Fusion (FUSION).
[21] Chee-Yee Chong,et al. Forty Years of Multiple Hypothesis Tracking - A Review of Key Developments , 2018, 2018 21st International Conference on Information Fusion (FUSION).
[22] Guillaume Hajduch,et al. A Multi-Task Deep Learning Architecture for Maritime Surveillance Using AIS Data Streams , 2018, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA).
[23] Moe Z. Win,et al. Message Passing Algorithms for Scalable Multitarget Tracking , 2018, Proceedings of the IEEE.
[24] Paolo Braca,et al. Scalable distributed change detection and its application to maritime traffic , 2017, 2017 IEEE International Conference on Big Data (Big Data).
[25] Zhe Xiao,et al. Maritime Traffic Probabilistic Forecasting Based on Vessels’ Waterway Patterns and Motion Behaviors , 2017, IEEE Transactions on Intelligent Transportation Systems.
[26] Ross B. Girshick,et al. Mask R-CNN , 2017, 1703.06870.
[27] Giorgio Battistelli,et al. Joint attack detection and secure state estimation of cyber‐physical systems , 2016, International Journal of Robust and Nonlinear Control.
[28] Luca Cazzanti,et al. Automated port traffic statistics: From raw data to visualisation , 2016, 2016 IEEE International Conference on Big Data (Big Data).
[29] Paolo Braca,et al. Modeling vessel kinematics using a stochastic mean-reverting process for long-term prediction , 2016, IEEE Transactions on Aerospace and Electronic Systems.
[30] Yu Huang,et al. Trajectory compression-guided visualization of spatio-temporal AIS vessel density , 2016, 2016 8th International Conference on Wireless Communications & Signal Processing (WCSP).
[31] Paolo Braca,et al. Consistent Estimation of Randomly Sampled Ornstein–Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction , 2016, IEEE Signal Processing Letters.
[32] Konstantinos Tserpes,et al. Employing traditional machine learning algorithms for big data streams analysis: The case of object trajectory prediction , 2016, J. Syst. Softw..
[33] Paolo Braca,et al. A Scalable Algorithm for Tracking an Unknown Number of Targets Using Multiple Sensors , 2016, IEEE Transactions on Signal Processing.
[34] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Roberto Cipolla,et al. Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding , 2015, BMVC.
[36] Anne-Laure Jousselme,et al. Data-driven detection and context-based classification of maritime anomalies , 2015, 2015 18th International Conference on Information Fusion (Fusion).
[37] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[38] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[39] Paolo Braca,et al. Validation of the Ornstein-Uhlenbeck route propagation model in the Mediterranean Sea , 2015, OCEANS 2015 - Genova.
[40] Paolo Braca,et al. Knowledge-Based Multitarget Ship Tracking for HF Surface Wave Radar Systems , 2015, IEEE Transactions on Geoscience and Remote Sensing.
[41] Erik Blasch,et al. Behavioral learning of vessel types with fuzzy-rough decision trees , 2014, 17th International Conference on Information Fusion (FUSION).
[42] Paolo Braca,et al. Maritime Surveillance Using Multiple High-Frequency Surface-Wave Radars , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[43] Michele Vespe,et al. Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction , 2013, Entropy.
[44] Leto Peel,et al. Maritime anomaly detection using Gaussian Process active learning , 2012, 2012 15th International Conference on Information Fusion.
[45] M. Vespe,et al. Unsupervised learning of maritime traffic patterns for anomaly detection , 2012 .
[46] Richard O. Lane,et al. Maritime anomaly detection and threat assessment , 2010, 2010 13th International Conference on Information Fusion.
[47] Ryan Riddolls,et al. A review of high frequency surface wave radar for detection and tracking of ships , 2010 .
[48] Anthony M. PONSFORD,et al. A review of high frequency surface wave radar for detection and tracking of ships , 2010, Turkish Journal of Electrical Engineering and Computer Sciences.
[49] Ba-Ngu Vo,et al. The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations , 2009, IEEE Transactions on Signal Processing.
[50] Mark R. Morelande,et al. Statistical analysis of motion patterns in AIS Data: Anomaly detection and motion prediction , 2008, 2008 11th International Conference on Information Fusion.
[51] Peter Willett,et al. Radar/AIS data fusion and SAR tasking for Maritime Surveillance , 2008, 2008 11th International Conference on Information Fusion.
[52] Bradley J. Rhodes,et al. Probabilistic associative learning of vessel motion patterns at multiple spatial scales for maritime situation awareness , 2007, 2007 10th International Conference on Information Fusion.
[53] Ba-Ngu Vo,et al. Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter , 2007, IEEE Transactions on Signal Processing.
[54] Ronald P. S. Mahler,et al. Statistical Multisource-Multitarget Information Fusion , 2007 .
[55] Allen M. Waxman,et al. Associative Learning of Vessel Motion Patterns for Maritime Situation Awareness , 2006, 2006 9th International Conference on Information Fusion.
[56] R. Mahler,et al. PHD filters of higher order in target number , 2006, IEEE Transactions on Aerospace and Electronic Systems.
[57] R. Mahler. Multitarget Bayes filtering via first-order multitarget moments , 2003 .
[58] Thiagalingam Kirubarajan,et al. Estimation with Applications to Tracking and Navigation , 2001 .
[59] Hans-Peter Kriegel,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.
[60] Edward H. Adelson,et al. PYRAMID METHODS IN IMAGE PROCESSING. , 1984 .
[61] Y. Bar-Shalom,et al. Tracking And Data Fusion A Handbook Of Algorithms By | , 2017 .
[62] Lauro Snidaro,et al. Context-Enhanced Information Fusion , 2016, Advances in Computer Vision and Pattern Recognition.
[63] D. Reid. An algorithm for tracking multiple targets , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.