Leonardo M. Millefiori

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Driven by real-world issues in maritime surveillance, we consider the problem of estimating the target state from a sequence of observations that can be imprecisely time-stamped. That is, the time between two consecutive observations can be affected by an unknown error or delay. We propose an adaptive filtering strategy able to sequentially detect the time(More)
Computational Maritime Situational Awareness (MSA) supports the maritime industry, governments, and international organizations with machine learning and big data techniques for analyzing vessel traffic data available through the Automatic Identification System (AIS). A critical challenge of scaling computational MSA to big data regimes is integrating the(More)
In this letter, we study the problem of estimating the long-run mean of the Ornstein-Uhlenbeck (OU) stochastic process and its effect on the long-term prediction of future vessel states, which is a crucial problem for Maritime Situational Awareness (MSA). We employ a sample mean estimator (SME) to estimate the key OU parameter from the observations,(More)
Long-term target state estimation of non-maneuvering targets, such as vessels under way in open sea, is crucial for maritime security. The dynamics of non-maneuvering targets is traditionally modeled with a white noise random process on the velocity, which is assumed to be nearly-constant. We show that this model might be an implausible hypothesis for a(More)
A second-order Ornstein-Uhlenbeck (OU) Process, or Mixed OU (MOU) process, provides a stable and stationary generalization to the well-established nearly-constant velocity (NCV) model - the workhorse kinematic model used in the target tracking community. The MOU process is useful in many settings including long time-horizon simulations, multiple-model(More)
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