This paper explores filtering methods to protect range-based localization systems from spoofing attacks on vehicles with directional receivers. It focuses on scenarios where multiple spoofers, potentially from unmanned vehicles, disrupt vehicle localization by strategically positioning themselves between the target and the transmitter. The paper introduces an Adaptive Resilience Navigation Filter (ARNF) that detects ongoing attacks, identifies compromised signals, and mitigates their effects using statistical hypothesis testing. Simulations demonstrate the ARNF’s effectiveness under realistic Global Navigation Satellite System conditions, comparing it with the 2-Stage Extended Kalman Fitter and an ideal Clairvoyant Extended Kalman Filter.

Maneuvering target tracking, where the system undergoes abrupt changes in the underlying motion model, can be challenging. We propose a model-based deep learning approach for prediction of maneuvering targets to exploit partial knowledge of the system physics-based models during training, without requiring an explicit characterization or fine tuning of model parameters. We formulate a supervised training scheme to learn the dynamics of state-space models and capture the jump processes governing model transitions by minimizing the prediction loss of an encoder-decoder network from model-based generated data. The effectiveness of the proposed method is demonstrated in two maneuvering target tracking scenarios using synthetic and real-world test data. The results show that the model-based encoder-decoder network achieves notably improved performance in terms of target prediction compared to conventional multiple-model solutions, especially when facing model inaccuracies, jumps, and dominant nonlinearities during target maneuvers.

Today, the maritime domain is at the cusp of a new era, driven by technological advances in automation, robotics, multisensor perception, and artificial intelligence (AI), together with digitalization and connectivity. Smart ship infrastructure and technology, remotely controlled and autonomous ship operation to improve safety, security, cost efficiency, and sustainability are the future of maritime transportation[1], representing now the engine of 90% of global trade[2]. Ships will soon benefit from recent developments in sensors, telecommunications, and computing technologies to turn the smart shipping revolution into reality[3]and[4], as it has already happened for autonomous vehicles such as driverless cars, aerial drones, unmanned (or remotely piloted) aircraft, and underwater vehicles.

In this paper we present how automatic maritime anomaly detection tools can be successfully applied in real-world situations such as the major event of the container vesselEver Given, which grounded in the Suez Canal on March 23rd 2021. The anomaly detector is designed to process the available sequence of Automatic Identification System (AIS) reports, information from ground-based or satellite radar systems if available, and contextual information defining the expected nominal behavior of navigation. A statistical hypothesis testing procedure is sequentially run to decide whether or not a deviation from the nominal behavior happened within a specific time period, for instance two consecutive data points. We show, based on the recorded AIS data from theEver Given, that the proposed detector could have been triggered and alerted to anomalous behavior fully 19 minutes before the grounding.

In this paper, we address the problem of predicting vessel trajectories based on Automatic Identification System (AIS) data. The goal is to learn the predictive distribution of maritime traffic patterns using historical data during the training phase, in order to be able to forecast future target trajectory samples online on the basis of both the extracted knowledge and the available observation sequence. We explore neural sequence-to-sequence models based on the Long Short-Term Memory (LSTM) encoder-decoder architecture to effectively capture long-term temporal dependencies of sequential AIS data and increase the overall predictive power. The experimental evaluation on a real-world AIS dataset demonstrates the effectiveness of sequence-to-sequence recurrent neural networks (RNNs) for vessel trajectory prediction and shows their potential benefits compared to model-based methods.

Piecewise mean-reverting stochastic processes have been recently proposed and validated as an effective model for long-term object prediction. In this paper, we exploit the Ornstein-Uhlenbeck (OU) dynamic model to represent an anomaly as any deviation of the long-run mean velocity from the nominal condition. This amounts to modeling the anomaly as an unknown switching control input that can affect the dynamics of the object. Under this model, the problem of joint anomaly detection and tracking can be addressed within the Bayesian random set framework by means of a hybrid Bernoulli filter (HBF) that sequentially estimates a Bernoulli random set (empty under nominal behavior) for the unknown long-run mean velocity, and a random vector for the kinematic state of the object. An additional challenge is represented by the fact that two extra parameters, i.e. the reversion rate and the noise covariance of the underlying OU process, need to be specified for Bayes-optimal prediction. We propose a multiple-model adaptive filter (MMA-HBF) for anomaly detection, tracking and simultaneous estimation of the OU unknown parameters. The effectiveness of these tools is demonstrated on a simulated maritime scenario.

This paper presents a solution to the problem of sequential joint anomaly detection and tracking of a target subject to switching unknown path deviations. Based on a dynamic model described by Ornstein-Uhlenbeck (OU) stochastic processes, the anomaly is represented by a target (e.g., a marine vessel) that deviates from a preset route by changing its nominal mean velocity. The Random Finite Set (RFS) framework is used to represent the switching nature of target's anomalous behavior in the presence of spurious measurements and detection uncertainty. Combining these two ingredients, the problem of jointly detecting target's path deviations and estimating its kinematic state can be formulated within the Bayesian framework, and analytically solved by means of a hybrid Bernoulli filter that sequentially updates the joint posterior density of the unknown OU velocity input (a Bernoulli RFS) and of the target's state random vector. We illustrate the effectiveness of the proposed filter, implemented in Gaussian-mixture form, in a simulated scenario of vessel tracking for maritime traffic monitoring.