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Found 15 papers, 1 papers with code
3D Localization and Tracking Methods for Multi-Platform Radar Networks
Simulated experiments show the benefit of the combined algorithm in comparison with the conventional baseline SPA-based MTT and the stand-alone ARCE localization, in a 3D sensing scenario.
Monitoring of Underwater Critical Infrastructures: the Nord Stream and Other Recent Case Studies
The explosions on September 26th, 2022, which damaged the gas pipelines of Nord Stream 1 and Nord Stream 2, have highlighted the need and urgency of improving the resilience of Underwater Critical Infrastructures (UCIs).
Large Deviations for Classification Performance Analysis of Machine Learning Systems
We study the performance of machine learning binary classification techniques in terms of error probabilities.
Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis
In other words, the classification error probability convergence to zero and its rate can be computed on a portion of the dataset available for training.
Recurrent Encoder-Decoder Networks for Vessel Trajectory Prediction with Uncertainty Estimation
Recent deep learning methods for vessel trajectory prediction are able to learn complex maritime patterns from historical Automatic Identification System (AIS) data and accurately predict sequences of future vessel positions with a prediction horizon of several hours.
Fusion of Sensor Measurements and Target-Provided Information in Multitarget Tracking
Here, we develop a statistical model and algorithm for tracking an unknown number of targets based on the probabilistic fusion of observations from two classes of data sources.
Cooperative Localization and Multitarget Tracking in Agent Networks with the Sum-Product Algorithm
This paper addresses the problem of multitarget tracking using a network of sensing agents with unknown positions.
Enhanced Target Localization with Deployable Multiplatform Radar Nodes Based on Non-Convex Constrained Least Squares Optimization
A new algorithm for 3D localization in multiplatform radar networks, comprising one transmitter and multiple receivers, is proposed.
Quickest Detection and Forecast of Pandemic Outbreaks: Analysis of COVID-19 Waves
The COVID-19 pandemic has, worldwide and up to December 2020, caused over 1. 7 million deaths, and put the world's most advanced healthcare systems under heavy stress.
Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks
Experimental results on vessel trajectories from an AIS dataset made freely available by the Danish Maritime Authority show the effectiveness of deep-learning methods for trajectory prediction based on sequence-to-sequence neural networks, which achieve better performance than baseline approaches based on linear regression or on the Multi-Layer Perceptron (MLP) architecture.
Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques
Maritime surveillance (MS) is of paramount importance for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies.
Space-based Global Maritime Surveillance. Part I: Satellite Technologies
Maritime surveillance (MS) is crucial for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies.
Quickest Detection of COVID-19 Pandemic Onset
This paper develops an easily-implementable version of Page's CUSUM quickest-detection test, designed to work in certain composite hypothesis scenarios with time-varying data statistics.
Classification-Aided Multitarget Tracking Using the Sum-Product Algorithm
Multitarget tracking (MTT) is a challenging task that aims at estimating the number of targets and their states from measurements of the target states provided by one or multiple sensors.
ODIN: Obfuscation-based privacy preserving consensus algorithm for Decentralized Information fusion in smart device Networks
ODIN is a privacy-preserving extension of the popular consensus gossip algorithm, that prevents distributed agents have direct access to the data while they iteratively reach consensus; agents cannot access even the final consensus value, but can only retrieve partial information, e. g., a binary decision.
Cryptography and Security
