Classification of Targets Using Optimized ISAR Euler Imagery

摘要

Various approaches exist to enable target classification through a decomposition of the polarimetric scattering matrix. Specifically, the Euler decomposition attempts to express the target scattering properties through more physically relevant parameters. Target classification in general has been limited by signature variability and the saturation of images by non-persistent scatterers. The Euler decomposition is sensitive to additional parameter ambiguities. It will be demonstrated how undesirable ambiguities may be identified and mitigated. Through the analysis of polarimetric ISAR signatures obtained in compact radar ranges at the University of Massachusetts Lowell Submillimeter Technology Laboratory and the U.S. Army National Ground Intelligence Center (NGIC), the cause of non-persistent scatters will be investigated. A proper characterization of non-persistence should lead to better optimization of the Euler decomposition, and thus improve target classification.