Use of 3D ship scatterer models from ISAR image sequences for target recognition

Traditionally, inverse synthetic aperture radar (ISAR) image frames are classified individually in an automatic target recognition system. When information from different image frames is combined, it is usually in the context of time-averaging to remove statistically independent noise fluctuations between images. The sea state induced variability of the ship target projections between frames, however, also provides additional information about the target, which can be used to construct a 3D representation of the target scatterer positions. In this paper, a method for classifying a ship based on 3D scatterer information from a sequence of 2D ISAR images is described. A preliminary classification result for simulated ISAR images of nine types of ship is also provided.