Experimental Characterization of a Multifunction X-Band AESA Radar Demonstrator

Modern naval surveillance demands multifunction radar systems capable of operating in cluttered and contested environments. This paper presents the experimental characterization of a compact, X-band Active Electronically Scanned Array (AESA) radar demonstrator. The system was evaluated in a realistic coastal field environment at Naval Support and Experimentation Centre (CSSN) and, specifically, its specialized institute, the G. Vallauri Institute, which has historical expertise in testing and evaluating the performance of operational sensors as well as those under development, using real maritime targets and an active noise jammer. The trials assessed three core functions: direction-of-arrival (DoA) estimation, adaptive jammer suppression using MVDR beamforming, and high-resolution Inverse Synthetic Aperture Radar (ISAR) imaging. The results confirm that the demonstrator successfully detects and localizes targets, effectively suppresses high-power interference, and generates clear ISAR images of non-cooperative vessels. These findings validate the multifunction performance of the AESA demonstrator, confirming its suitability for advanced naval surveillance applications.

A Unified Maximum-Likelihood Framework for 3D InISAR Phase Unwrapping with Outlier Rejection

This paper presents a novel mathematical framework for phase unwrapping in three-dimensional interferometric ISAR (3D InISAR) imaging. The approach works on a scatterer-by-scatterer basis and does not rely on any spatial continuity assumptions, making it suitable for sparse point clouds. The formulation is derived from the Mixed-Integer Least Squares (MILS) theory, an optimal maximum-likelihood framework for joint estimation of integer and real unknowns in the presence of Gaussian noise. This provides a unified way to handle generic sensor geometries, multi-baseline, multi-frequency, or hybrid setups. The method also produces a natural a posteriori quality metric for each unwrapped phase, which can be used to build a statistical test to reject outliers. The algorithm is simple to implement and has a computational cost suitable for operational systems. This paper presents the theoretical foundations of the framework and a first validation study on a standard L-shaped dual-frequency setup using Monte Carlo simulations. Results show that the proposed framework enables reliable 3D reconstruction in challenging ambiguity conditions.