Accelerated translational motion compensation with contrast maximisation optimisation algorithm for inverse synthetic aperture radar imaging

Abstract

Range alignment of traditional translational motion compensation for inverse synthetic aperture radar imaging generally cannot be implemented accurately under low signal-to-noise ratio, resulting in the following phase adjustment invalid. In this study, a novel accelerated translational motion compensation with contrast maximisation optimisation algorithm is proposed. Translational motion is first modelled as a parametric finite order polynomial. The translational motion property can be compactly expressed by a polynomial coefficient vector. Meanwhile, the image contrast is utilised to estimate the polynomial coefficient vector based on the maximum contrast optimisation, implemented by Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. BFGS is an effective quasi-Newton algorithm, yielding fast convergence and small computational complexity. Moreover, a method called pseudo Akaike information criterion is also proposed to determine the polynomial order adaptively. Both simulated and real data experiments are provided for a clear demonstration of the proposed algorithm.