A Unified Maximum-Likelihood Framework for 3D InISAR Phase Unwrapping with Outlier Rejection
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.
Cite
@article{arxiv.2605.28431,
title = {A Unified Maximum-Likelihood Framework for 3D InISAR Phase Unwrapping with Outlier Rejection},
author = {Matteo Pardi and Francesco Mancuso and Elisa Giusti and Marco Martorella},
journal= {arXiv preprint arXiv:2605.28431},
year = {2026}
}
Comments
6 pages, 4 figures. Accepted for publication in the Proceedings of the 2026 IEEE Radar Conference (RadarConf26)