Solving 3D Radar Imaging Inverse Problems with a Multi-cognition Task-oriented Framework
This addresses the need for task-specific radar imaging in applications like scattering diagnosis and screening, but it is incremental as it builds on existing inverse problem methods with task-oriented modifications.
This work tackles the problem of 3D radar imaging inverse problems, where current methods produce undifferentiated results with task-dependent information retrieval loss, by proposing a task-oriented framework that outperforms existing methods in task-dependent information retrieval.
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.