Pseudo-Zernike-based multi-pass automatic target recognition from multi-channel synthetic aperture radar

The capability to exploit multiple sources of information is of fundamental importance in a battlefield scenario. Information obtained from different sources, and separated in space and time, provides the opportunity to exploit diversities tomitigate uncertainty. In this study, the authors address the problem of automatic target recognition (ATR) from syntheticaperture radar platforms. The author's approach exploits both channel (e.g. polarisation) and spatial diversity to obtainsuitable information for such a critical task. In particular they use the pseudo-Zernike moments (pZm) to extract featuresrepresenting commercial vehicles to perform target identification. The proposed approach exploits diversities and invariantproperties of pZm leading to high confidence ATR, with limited computational complexity and data transfer requirements.The effectiveness of the proposed method is demonstrated using real data from the Gotcha dataset, in different operationalconfigurations and data source availability.