Developing a Geometric Deformable Model for Radar Shape Inversion

Abstract

In this paper, we develop a radar-based dense scene reconstruction model that extracts shape information embedded in the radar return signal. Our method uses a deformable shape evolution approach which seeks to match the received signal to a computed forward model based on the evolving shape. This allows us to directly incorporate geometric considerations of the shape into the problem formulation, such as smoothness and self-occlusions. Iterations start with an initial shape which is gradually deformed until its image under the forward model gets sufficiently close to the actual measured signal. For this purpose, we employ the technique of stretch processing to extract geometric properties of the shape from radar return signal. This yields a smooth and purely geometric cost functional by which shape inversion can be robustly performed via gradient-based minimization algorithms. Synthetic simulations with a polygonal shape model show the promise of this type of approach on some challenging shapes.