Synthetic Aperture Radar Imaging Autofocus Algrithm Research

Synthetic aperture radar (SAR) provides a means of producing high-resolutionn microwave images using an antenna of small size. SAR images have wide applications in surveillanece,remote sensing, and mapping of the surfaces of both the Earth and other planets. The defining characteristic of SAR is its coherent processing of data collected by an antenna at locations along a trajectory in space. In principle,an image of extraordinary resolution can be produced.However, imprecise position measurements associated with data collected at each location cause phase errors that, in turn, cause the reconstructed image to suffer distortion, sometimes so severe that the image is completely unrecognizable. Autofocus algorithms apply signal processing techniques to restore the focused image.The autofocus problem in synthetic aperture radar imaging amounts to estimating unknown phase errors caused by unknown platform or target motion. At the heart of three state-of-the-art autofocus algorithms, namely, phase gradient autofocus, multichannel autofocus (MCA), and Fourier domain multichannel autofocus (FMCA), is the solution of a constant modulus quadratic program (CMQP). Currently, these algorithms solve a CMQP by using an eigenvalue relaxation approach or semidefinite relaxation approach.This paper proposes a method that does not apply relaxation approach but solve the CMQP problem directly,i.e. Lagrange Programming Neural network(LPNN).The algorithm is based on lagrangian multiplier as well as neural network theory,and it can be used to solve nonlinear optimization model with constraint condition,so this paper applies LPNN in imaging autofoc~us problem.In section simulation,this paper will present the results of three mentioned algorithm,and analyze their performance in detail.