Solving 3D Radar Imaging Inverse Problems with a Multi-cognition Task-oriented Framework
Abstract
This work focuses on 3D Radar imaging inverse problems. Current methods obtain undifferentiated results that suffer task-depended information retrieval loss and thus don't meet the task's specific demands well. For example, biased scattering energy may be acceptable for screen imaging but not for scattering diagnosis. To address this issue, we propose a new task-oriented imaging framework. The imaging principle is task-oriented through an analysis phase to obtain task's demands. The imaging model is multi-cognition regularized to embed and fulfill demands. The imaging method is designed to be general-ized, where couplings between cognitions are decoupled and solved individually with approximation and variable-splitting techniques. Tasks include scattering diagnosis, person screen imaging, and parcel screening imaging are given as examples. Experiments on data from two systems indicate that the pro-posed framework outperforms the current ones in task-depended information retrieval.
Cite
@article{arxiv.2211.14989,
title = {Solving 3D Radar Imaging Inverse Problems with a Multi-cognition Task-oriented Framework},
author = {Xu Zhan and Xiaoling Zhang and Mou Wang and Jun Shi and Shunjun Wei and Tianjiao Zeng},
journal= {arXiv preprint arXiv:2211.14989},
year = {2022}
}