Solving 3D Radar Imaging Inverse Problems with a Multi-cognition Task-oriented Framework

TL;DR

This paper introduces a task-oriented framework for 3D radar imaging that improves information retrieval by customizing the imaging process to specific task demands, outperforming existing methods.

Contribution

A novel multi-cognition regularized framework that decouples and solves different task-specific imaging demands for 3D radar data.

Findings

Outperforms current methods in task-dependent information retrieval

Effective in scattering diagnosis and imaging tasks

Generalized approach applicable to multiple radar imaging scenarios

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.