Discrete 2-D system identification for imaging rotating radar targets

Abstract A new approach to high-resolution radar imaging is presented. The starting is the notion that there exists an image of the target which needs to be identified. This image consists of a finite array of pixel elements, each with a magnitude and phase representing the complex reflectivity of target scatterers. The radar observation process is then modelled as a sequence of matrix operations applied to the image. We first treat this problem in a general way, then impose practical constraints to finally deal with the problem of a rotating target. One solution for recovering the image from the measurements in this case is obtained by inverting the matrix operations, although the rotation rate of the target is required. A second solution based on maximum likelihood techniques is derived and the connection with the pseudoinverse is shown. When the rotation rate of the target is unknown, the maximum likelihood solution can still be found through minimization of a non-convex matrix trace cost function. It is also observed that for unknown target rotation rate the maximum likelihood solution gives the same image estimate as does the conventional Fourier transform solution. Some results of applying this identification approach to simulated and experimental data are presented. Various extensions are proposed, including a recursive solution.