Maximum Likelihood Estimation of Antenna Positions in Short-Range Synthetic Aperture Radar

Abstract

In this article we present first investigations on the estimation of antenna positions from high-resolution radar data for the use in synthetic aperture radar autofocus. The approach is based on a maximum likelihood estimation utilizing the position of known reference targets or strong scattering centers within the scene and operates on a pulse-to-pulse basis. Thus, the proposed method is independent of the antenna trajectories and imaging algorithm and supports monostatic as well as bistatic systems. While the estimation procedure involves optimization of a non-convex objective function, encouraging performance is achieved using a particle swarm optimization routine. Performance and robustness of the approach against surface clutter and noise signals is demonstrated by a Monte-Carlo analysis. The influence of uncertainties in the position of the reference targets is shown to be of crucial importance and thus offers potential for further development.