Abstract
The lack of openly-available bistatic imagery and analysis of the unique artefacts which occur within it is a significant barrier to developing automatic target recognition methods for such systems. This paper addresses these issues by presenting a simulation methodology for obtaining bistatic SAR imagery of ground vehicle targets and investigating the features that occur within this imagery. Several effects unique to the bistatic case are presented, and the performance degradation of a classifier at several bistatic angles is empirically demonstrated. A version of the final database will be publicly released to promote wider research into this challenge.
Michael Woollard
PhD Student
My research interests include synthetic aperture radar, automatic target recognition and electronic warfare.