arXiv subjects

Alessandro D'Ortenzio

Publications and source records attributed to Alessandro D'Ortenzio.

3 recordsLinked to original sources

arXiv2026

GNSS-free quantum gravity-aided navigation and fine-scale marine surveying with a strapdown quantum gravimeter

Global navigation satellite systems (GNSS) are often disrupted or unavailable at sea, and unaided inertial navigation systems (INS) drift without correction. Quantum-sensing-based gravity map matching offers a passive, infrastructure-free aid, but field demonstrations of GNSS-free quantum gravimetric navigation have not been reported. Here we perform gravity map matching and fine-resolution gravity survey with a mobile quantum gravimeter aboard a 29 m surface vessel. We hybridize an atomic sensor with a classical accelerometer for bias stabilization and independently mechanize a navigation-grade IMU, all installed in an uncontrolled cabin with no environmental stabilization or calibration. Operated in both gimbaled and strapdown configurations over identical traversals, the hybrid sensor corrected the inertial solution over an 83 km maritime trajectory by referencing locally measured gravity to a satellite-derived anomaly map. Gravity-aiding constrains INS drift and delivers bounded positioning at nautical-mile-level accuracy, with GNSS excluded throughout the measurement chain. In a separate GNSS-referenced mode, the same system surveyed coastal routes up to Sea State 4, achieving mGal-level agreement with gravimetric maps and sub-mGal repeatability and stability, with gimbaled and strapdown operation performing comparably. Resolved anomalies reach an along-track scale of ~300 m, 50X finer than the satellite map's half-power wavelength. A 56 h stationary test shows atom referencing lowers long-term drift ~70X versus the classical channel alone. These results provide the first same-instrument comparison of gimbaled and strapdown mobile quantum gravimetry and the first fully GNSS-independent gravity-map-matching navigation demonstration using a quantum gravimeter, pointing toward compact, autonomous-platform-ready quantum sensing for GNSS-denied maritime navigation and survey.

quant-ph↗

arXiv2022

Adaptive mixture approximation for target tracking in clutter

Target tracking represents a state estimation problem recurrent in many practical scenarios like air traffic control, autonomous vehicles, marine radar surveillance and so on. In a Bayesian perspective, when phenomena like clutter are present, the vast majority of the existing tracking algorithms have to deal with association hypotheses which can grow in the number over time; in that case, the posterior state distribution can become computationally intractable and approximations have to be introduced. In this work, the impact of the number of hypotheses and corresponding reductions is investigated both in terms of employed computational resources and tracking performances. For this purpose, a recently developed adaptive mixture model reduction algorithm is considered in order to assess its performances when applied to the problem of single object tracking in the presence of clutter and to provide additional insights on the addressed problem.

stat.AP↗

arXiv2022

Fixed-point iterations for several dissimilarity measure barycenters in the Gaussian case

In target tracking and sensor fusion contexts it is not unusual to deal with a large number of Gaussian densities that encode the available information (multiple hypotheses), as in applications where many sensors, affected by clutter or multimodal noise, take measurements on the same scene. In such cases reduction procedures must be implemented, with the purpose of limiting the computational load. In some situations it is required to fuse all available information into a single hypothesis, and this is usually done by computing the barycenter of the set. However, such computation strongly depends on the chosen dissimilarity measure, and most often it must be performed making use of numerical methods, since in very few cases the barycenter can be computed analytically. Some issues, like the constraint on the covariance, that must be symmetric and positive definite, make it hard the numerical computation of the barycenter of a set of Gaussians. In this work, Fixed-Point Iterations (FPI) are presented for the computation of barycenters according to several dissimilarity measures, making up a useful toolbox for fusion/reduction of Gaussian sets in applications where specific dissimilarity measures are required.

stat.CO↗