Abstract- When a moving object is imaged with conventional synthetic aperture radar (SAR) the result is a displaced smear. This is due to the extra information the object?s motion imparts to the radar return. If the motion is rich enough ? and it usually is ? there should be a possibility of forming a 3D image of the object. This involves understanding the way the radar data are arranged in phase space. The data lie on a convoluted surface that occupies three dimensions rather than the two-dimensional plane used to form conventional SAR images. To achieve three-dimensional images the data must be extrapolated from the surface into a volume. In this phase space, there is a great deal of structure and therefore the possibility of extrapolating to a volume of data. The effort is motivated by the potential value of the three-dimensional image products that become available from the data volume. The volume can produce aspect, aspect, range data that results in views of the moving object not available previously. These include radar images from the point of view of the radar rather than orthogonal to it, range images similar to ladar, orthogonal three-view images similar to mechanical drawings, and other image products. General Dynamics AIS, supported by the Air Force, has been investigating exploiting moving targets whose returns are captured by conventional SAR systems. The result is a processing system that can extract the detailed three-dimensional motions of a moving object. This system is called Three-Dimensional Motion and Geometric Information (3DMAGI). When the estimated motions are used to motion compensate the radar data to the moving target, the resulting data surface deviates radically from the conventional SAR image plane. No simple function, such as a quadratic, can create this data surface. Therefore no simple focusing scheme can accurately focus this data into a standard 2D SAR image. By using the measured data surface some approximations can be made that will produce conventional-looking radar images. These, however, are likely to include partial side or end views and the azimuth (and / or height) resolution will depend on the shape of the data surface (motion of the object), and may greatly exceed the range resolution defined by the bandwidth of the radar system. This paper reports on work done with a full volume of data from the National Ground Intelligence Center and vehicle trajectories measured by an inertial system on a moving vehicle. Its goal is to determine how to best use the rich data available from advanced processing to produce images and image products that will simplify the task of exploiting the radar image. The data and sample trajectory are described as well as how they are used to emulate the result of 3DMAGI processing. The work consists of investigations into the methods of creating a 3D data volume that matches the NGIC chamber collection, starting from a small subset defined by the data surface which lies in the full volume. How much extrapolation is needed to get acceptable results is the first question posed. From there, the question of just what methods yield the best results is examined. Limitations of various methods are explained with examples. Comparisons of each method of extrapolation to the original data volume are presented to give an indication of progress toward the goal.

Along-track interferometry (ATI) is an interferometric synthetic aperture radar technique that can be used to measure Earth-surface velocities. As such, the ATI technique holds promise for the detection of slowly moving ground targets. The models often used to characterize ATI performance were developed mainly in the context of mapping ocean currents, however, and they do not necessarily apply to the case of discrete, moving ground targets amidst clutter. In this paper, we provide expressions for more accurately modeling the behavior of an ATI system in the context of ground moving target indication. Analysis and design equations are given for topics including target defocus, signal-to-noise and signal-to-clutter ratios, interferometric correlation, interferometric phase bias, target detection, geolocation accuracy, and area coverage rate.

Synthetic Aperture Radar (SAR) systems are designed to produce high quality imagery of stationary target on the ground. These systems are not designed to handle moving targets and perform poorly in the areas of detecting and imaging moving targets. This paper will present advanced techniques developed to handle the detection and refocusing of moving targets for SAR systems.

One of the most critical steps of ISAR image processing is the motion compensation, also known as ISAR image focusing. For non-cooperative targets and especially when external data are not available, autofocusing techniques must be used. Among all the techniques developed for ISAR image autofocusing, the Contrast Based Autofocusing Technique has been lately proposed by the authors. One of the critical aspects of such a technique is represented by the solution of an optimisation problem. Because the Image Contrast is generally a multimodal function, classic optimisation methods do not reach the best result. In this paper a new solution of the optimisation problem is given by means of genetic algorithms. Moreover, the model of the focusing point phase history is extended to a generic polynomial and the problem of defining the polynomial order is addressed and heuristically solved. The effectiveness of the algorithm improvements, due to both the use of genetic algorithms and to the signal model extension is tested by means of real data.