Abstract. This paper presents a novel a priori knowledgebased algorithm for traffic monitoring applications. The powerful post-Doppler space-time adaptive processing (PD STAP) is combined with a known road network obtained from the freely available OpenStreetMap (OSM) database. The road information is applied after the PD STAP for recognizing and rejecting false detections, and moreover, for repositioning the vehicles detected in the vicinity of the roads. The algorithm presents great potential for real-time processing, decreased hardware complexity and low costs compared to state-of-the-art systems. The processor was tested using real multi-channel data acquired by DLR's airborne system F-SAR. The experimental results are shown and discussed, and the novelties are highlighted (e.g., the benefits of using a priori knowledge information).

Ship detection is an essential maritime security requirement. Current state-of-the-art synthetic aperture radar (SAR) based ship detection methods employ fully focused images. The time-consuming processing efforts required to generate these images make them generally unsuitable for real time applications. This paper proposes a novel real time oriented ship detection strategy applicable to range-compressed (RC) radar data acquired by an airborne radar sensor during linear, circular and arbitrary flight tracks. A constant false alarm rate (CFAR) detection threshold is computed in the range-Doppler domain using suitable distribution functions. Detection in range-Doppler has the advantage that principally even small ships with a low radar cross section (RCS) can be detected if they are moving fast enough so that the ship signals are shifted to the exo-clutter region. In order to determine a robust threshold, the ocean statistics have to be described accurately. Bright target peaks in the background ocean data bias the statistics and lead to an erroneous threshold. Therefore, an automatic ocean training data extraction procedure is proposed in the paper. It includes (1) a novel target pre-detection module that removes the bright peaks from the data already in time domain, (2) clutter normalization in the Doppler domain using the remaining samples, (3) ocean statistics estimation and (4) threshold computation. Various sea clutter models are investigated and analyzed in the paper for finding the most suitable models for the RC data. The robustness and applicability of the proposed method is validated using real linearly and circularly acquired radar data from DLR’s (Deutsches Zentrum für Luft- und Raumfahrt) airborne F-SAR system.

Synthetic Aperture Radar (SAR) is an established remote sensing technique that can robustly provide high-resolution imagery of the Earth’s surface. However, current space-borne SAR systems are limited, as a matter of principle, in achieving high azimuth resolution and a large swath width at the same time. Digital beamforming (DBF) has been identified as a key technology for resolving this limitation and provides various other advantages, such as an improved signal-to-noise ratio (SNR) or the adaptive suppression of radio interference (RFI). Airborne SAR sensors with digital beamforming capabilities are essential tools to research and validate this important technology for later implementation on a satellite. Currently, the Microwaves and Radar Institute of the German Aerospace Center (DLR) is developing a new advanced high-resolution airborne SAR system with digital beamforming capabilities, the so-called DBFSAR, which is planned to supplement its operational F-SAR system in near future. It is operating at X-band and features 12 simultaneous receive and 4 sequential transmit channels with 1.8 GHz bandwidth each, flexible DBF antenna setups and is equipped with a high-precision navigation and positioning unit. This paper aims to present the DBFSAR sensor development, including its radar front-end, its digital back-end, the foreseen DBF antenna configuration and the intended calibration strategy. To analyse the status, performance, and calibration quality of the DBFSAR system, this paper also includes some first in-flight results in interferometric and multi-channel marine configurations. They demonstrate the excellent performance of the DBFSAR system during its first flight campaigns.

Space-time adaptive processing (STAP) of multichannel radar data is an established and powerful method for detecting ground moving targets, as well as for estimating their geographical positions and line-of-sight velocities. Crucial steps for practical applications are: 1) the appropriate and automatic selection of the training data and 2) the periodic update of these data to take into account the change of the clutter statistics over space and time. Improper training data and contamination by moving target signals may result in a decreased clutter suppression performance, an incorrect constant false alarm rate threshold, and target cancelation by self-whitening. In this paper, two conventional and two novel methods for training data selection are evaluated and compared using real four-channel X-band radar data acquired with DLR's airborne sensor F-SAR. In addition, a module for rejecting potential moving target signals and strong scatterers from the training data is proposed and discussed. All methods are evaluated for a conventional post-Doppler (PD) STAP processor and for a particular PD STAP that uses an a priori known road map.

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.

Multichannel calibration is essential for detecting moving targets and for estimating their positions and velocities accurately. This article presents a fast and efficient calibration algorithm for the along-track multichannel systems, in particular for space-time adaptive processing (STAP) techniques. The proposed algorithm corrects the phase and magnitude offsets of the receive channels and also takes into account the Doppler centroid variation (e.g., caused by atmospheric turbulences) along the slant range and the azimuth time. The knowledge of the Doppler centroid variation is especially important for an accurate clutter covariance matrix estimation, which is required by STAP for efficient clutter suppression. Important calibration parameters and offsets are estimated directly from the range-compressed training data. The proposed algorithm is evaluated based on real multichannel X-band radar data acquired with DLR's airborne system F-SAR and compared with the state-of-the-art digital channel balancing technique. The experimental results show the potential of the proposed calibration algorithm toward real-time applications.

Abstract. Synthetic aperture radar (SAR) is an efficient solution for road traffic monitoring due to its high spatial resolution and independence from daylight and weather conditions. In this sense, a number of ground moving target indication (GMTI) algorithms have been developed, whereas their robustness is often achieved with high costs, increased hardware complexity and high computational burden. This paper presents a fast GMTI processor that blends the powerful post-Doppler space-time adaptive processing (PD STAP) with an a priori known road map and digital elevation model (DEM). The algorithm presents great potential for real-time processing, decreased hardware complexity and low costs compared to state-of-the-art systems. It is tested using real 4-channel X-band radar data acquired with the DLR's airborne F-SAR.

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 AIS (automatic identification system) data of real ships. Radar data from the DLR's multichannel airborne DBFSAR system are used to demonstrate the robustness and applicability of the proposed methods in real maritime scenarios.