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arXiv · 2605.28431

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

Matteo Pardi·Francesco Mancuso·Elisa Giusti·Marco Martorella
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Abstract

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.

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Matteo Pardi, Francesco Mancuso, Elisa Giusti, Marco Martorella. 2026-05-27. A Unified Maximum-Likelihood Framework for 3D InISAR Phase Unwrapping with Outlier Rejection. https://arxiv.org/abs/2605.28431

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