For maritime security applications it is advantageous or even required, that additionally to the geographical positions and moving directions the dimensions of the detected ships are available for subsequent classification and recognition purposes. In this paper a fast and robust method for size and heading angle estimation of ships in synthetic aperture radar (SAR) and inverse SAR (ISAR) images is proposed. The novel method leverages the eigenvalue decomposition of the detected ship pixel positions for determining the just mentioned parameters. The effectiveness of the proposed method is assessed against several state-of-the-art methods by using real X-band SAR images acquired with the German TerraSAR-X radar satellite in stripmap mode. The achieved accuracies of the proposed method are better than the ones obtained with the considered state-of-the-art methods and, as a further benefit, the computation time is also significantly shorter.

Near real-time ship monitoring is crucial for ensuring safety and security at sea. Established ship monitoring systems are the automatic identification system (AIS) and marine radars. However, not all ships are committed to carry an AIS transponder and the marine radars suffer from limited visibility. For these reasons, airborne radars can be used as an additional and supportive sensor for ship monitoring, especially on the open sea. State-of-the-art algorithms for ship detection in radar imagery are based on constant false alarm rate (CFAR). Such algorithms are pixel-based and therefore it can be challenging in practice to achieve near real-time detection. This letter presents two object-oriented ship detectors based on the faster region-based convolutional neural network (R-CNN). The first detector operates in time domain and the second detector operates in Doppler domain of airborne Range-Compressed (RC) radar data patches. The Faster R-CNN models are trained on thousands of real X-band airborne RC radar data patches containing several ship signals. The robustness of the proposed object-oriented ship detectors is tested on multiple scenarios, showing high recall performance of the models even in very dense multitarget scenarios in the complex inshore environment of the North Sea.

Direction-of-arrival (DOA) angle estimation is a prerequisite for projecting the airborne radar-based target detections to ground via a geocoding operation. Most state-of-the-art DOA angle estimation methods assume one detection per target. These methods cannot be applied one-to-one on extended targets like ships because individual ships in high-resolution data are generally composed of several distinct radar detections. In this letter, four methods for estimating the DOA angle for extended targets are formulated and discussed. The performance of the proposed methods is assessed by using simultaneously acquired automatic identification system (AIS) data of real ships. Radar data from the DLR’s multichannel airborne digital beamforming synthetic aperture radar (DBFSAR) system are used to demonstrate the robustness and applicability of the proposed methods in real maritime scenarios.

Accurate position estimation of ground-moving targets is a crucial requirement for any radar-based surveillance system. For a multi-channel airborne radar, the target position on the ground can be accurately obtained by estimating the direction-of-arrival (DOA) angle of the moving targets. However, in practice, the aircraft motion caused by atmospheric turbulence tilts the antenna array and introduces undesired phase differences among the multiple receive channels. As a result, the accuracy of the estimated DOA angles can be severely affected. This letter presents a robust and efficient algorithm that corrects the undesired phase differences among the multiple receive channels. By doing this, accurate DOA angles and, therefore, accurate target positions on the ground can be estimated. Important inputs of the proposed algorithm are the precise absolute positions of the receive channels and the elevation of the terrain. The performance of the proposed algorithm is validated using simulated data as well as radar data acquired with the DLR’s multi-channel airborne system with digital beamforming capabilities digital beamforming SAR (DBFSAR).

Ship tracking facilitates a comprehensive insight into maritime traffic situations and ensures its safety and security. However, with the current operational surveillance systems, detecting several maritime threats is still a major challenge. In this article, we propose a supportive ship tracking concept using an airborne-based radar sensor. The proposed tracking algorithm is suitable for dense multitarget scenarios. Tracking is performed in the range-Doppler domain. The primary advantage of using the range-Doppler domain is that ships even with low radar cross section moving with certain line-of-sight velocity appear out of the clutter region, thus improving their detectability. In addition, a powerful track management system is also developed to handle false targets. The simulated and real experimental results from the DLR’s airborne radar sensors, F-SAR and DBFSAR, are presented to prove the concept.