We propose a new autofocus approach for the backprojection reconstruction algorithm to compute high-quality synthetic aperture radar images of non-linearly moving and maneuvering ships. In contrast to the state-of-the-art autofocus techniques, our approach allows a long coherent processing interval even in the case of a rough sea, which improves the image quality. An improved image quality enables the classification of ships in airborne synthetic aperture radar (SAR) images. For this purpose, we decompose the image into subimages and estimate pulse-by-pulse a phase error for each subimage by maximizing subimage sharpness. A regularized Levenberg-Marquardt algorithm guarantees a smooth phase correction on subimage level. By correcting the subsequent range distances from the flight path to all pixels using the currently estimated phase errors, sharp images of maneuvering ships with arbitrary velocities can now be reconstructed. The evaluation of our proposed ship autofocus technique on the basis of real airborne X-band data shows that our approach leads to a visible improvement of image quality in comparison with the state-of-the-art techniques. Given these results, even an automatic ship classification based on radar images might be possible in the future.

Global Navigation Satellite Systems provide autonomous vehicles with precise position information through the process of position augmentation. This paper presents a series of performance tests aimed to compare the position accuracy of augmentation techniques such as classical Differential Global Navigation Satellite System, Real-time Kinematic and Real-time eXtended. The aim is to understand the limitations and choose the best position augmentation technique in order to obtain accurate, trustworthy position estimates of a vehicle in urban environments. The tests are performed in and around the German cities of Wuppertal and Duesseldorf, using a vehicle fitted with the navigation system POS-LV 220, developed by Applanix Corporation. In order to evaluate the real-time performance of position augmentation techniques in a highly challenging environment, a total of four test regions are selected. The four test regions are characterized mainly by uneven terrain with tall buildings around the University of Wuppertal, flat terrain with roads of varying width in the city centre of Wuppertal and Duesseldorf and flat terrain in a tunnel section located in the city of Wuppertal. The performances of the different position augmentation are compared using a Root Mean Square (RMS) error estimate obtained as an output from the Applanix system. Furthermore, a High-Definition map of the environment is used for the purpose of model validation, which justifies the use of RMS error estimate as an evaluation metric for the performance analysis tests. According to the performance tests carried out as per the conditions specified in this paper, the Real-time eXtended (RTX) position augmentation method enables to obtain a more robust position information of the vehicle than Real-time Kinematic (RTK) method, with a typical accuracy of a few centimeter in an urban environment.