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Your search keyword '"Augusto Aubry"' showing total 79 results
Start Over Author "Augusto Aubry" Publication Type Academic Journals
79 results on '"Augusto Aubry"'

1. Sparse DOA Estimation With Polarimetric Arrays

Author

Augusto Aubry, Marco Boddi, Antonio De Maio, and Massimo Rosamilia

Subjects
DOA estimation, high resolution, polarimetry, sparse methods, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

This paper addresses the Direction-of-Arrival (DOA) estimation problem using a narrowband polarimetric array sensing system. The considered receiving equipment is composed of two sub-arrays of sensors with orthogonal polarizations. By suitably modeling the received signal via a sparse representation (accounting for the multiple snapshots and the polarimetric array manifold structure), two iterative algorithms, namely Polarimetric Sparse Learning via Iterative Minimization (POL-SLIM) and Polarimetric Sparse Iterative Covariance-based Estimation (POL-SPICE), are devised to accomplish the estimation task. The proposed algorithms provide accurate DOA estimates while enjoying nice (rigorously proven) convergence properties. Numerical analysis shows the effectiveness of POL-SLIM and POL-SPICE to successfully locate signal sources in both passive sensing applications (with large numbers of collected snapshots) and radar spatial processing, also in comparison with single-polarization counterparts as well as theoretical benchmarks.

Published
2024
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2. Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis

Author

Paolo Braca, Leonardo M. Millefiori, Augusto Aubry, Stefano Marano, Antonio De Maio, and Peter Willett

Subjects
Machine learning, deep learning, large deviations principle, exact asymptotics, statistical hypothesis testing, Fenchel-Legendre transform, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

We study the performance of Machine Learning (ML) classification techniques. Leveraging the theory of large deviations, we provide the mathematical conditions for a ML classifier to exhibit error probabilities that vanish exponentially, say $\exp (-n\,I)$, where $n$ is the number of informative observations available for testing (or another relevant parameter, such as the size of the target in an image) and $I$ is the error rate. Such conditions depend on the Fenchel-Legendre transform of the cumulant-generating function of the Data-Driven Decision Function (D3F, i.e., what is thresholded before the final binary decision is made) learned in the training phase. As such, the D3F and the related error rate $I$ depend on the given training set. The conditions for the exponential convergence can be verified and tested numerically exploiting the available dataset or a synthetic dataset generated according to the underlying statistical model. Coherently with the large deviations theory, we can also establish the convergence of the normalized D3F statistic to a Gaussian distribution. Furthermore, approximate error probability curves $\zeta _{n} \exp (-n\,I)$ are provided, thanks to the refined asymptotic derivation, where $\zeta _{n}$ represents the most representative sub-exponential terms of the error probabilities. Leveraging the refined asymptotic, we are able to compute an accurate analytical approximation of the classification performance for both the regimes of small and large values of $n$. Theoretical findings are corroborated by extensive numerical simulations and by the use of real-world data, acquired by an X-band maritime radar system for surveillance.

Published
2022
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