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.

Road surface roughness is a major factor that is responsible for the skid resistance of vehicles and, thus, road safety. Therefore, it needs to be monitored regularly to ensure that the roughness values are in the optimal range and to perform maintenance actions when needed. Synthetic aperture radar (SAR) backscatter is sensitive to surface roughness and can provide large-scale estimates of road surface roughness. In this letter, a semi-empirical model for estimating road surface roughness using high-resolution spaceborne X-band SAR datasets from Germany’s TerraSAR-X satellite is proposed for the first time. The method is capable of handling the low signal-to-noise ratio (SNR) of spaceborne SAR. To enhance the reliability of the results obtained from rather low SNR datasets, techniques such as SNR thresholding, multi-dataset fusion, and automatic road extraction were implemented. The study’s results show good agreement with ground truth data.

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.

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.

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.

In this paper, a novel method for traffic monitoring using a spaceborne dual-platform synthetic aperture radar system is presented. The chosen along-track baseline between both platforms needs to be on the order of ten to several tens of kilometers, resulting in a time lag of several seconds between the target observations. With the proposed method, the true geographical positions, the velocities, and the moving directions of the detected moving targets can be estimated with high accuracy. The method has been verified and evaluated using experimental data acquired with the German TerraSAR-X/TanDEM-X radar satellite formation. The results are compared with ground truth reference data.

The paper presents a preliminary statistical evaluation of the sea clutter returns imaged by a spaceborne synthetic aperture radar (SAR) mission as TerraSAR-X. Two polarimetric data takes, Dual-polarized (Dual-Pol) and Quad-polarized (Quad-Pol), covering the range of incidence angles from 30 to 32 degrees, are considered for the stochastic characterization. The results show that the K-distribution, extendedly assumed statistics for high resolution radar sea clutter imaging, fits well the data's amplitude. This information jointly with the inverted sea spectrum are used to simulate SAR images of realistic scenarios that can be exploited for proper performance evaluation of ground moving target indication (GMTI) techniques and missions for maritime surveillance.

During the last decade, synthetic aperture radar (SAR) became an indispensable source of information in Earth observation. This has been possible mainly due to the current trend toward higher spatial resolution and novel imaging modes. A major driver for this development has been and still is the airborne SAR technology, which is usually ahead of the capabilities of spaceborne sensors by several years. Today's airborne sensors are capable of delivering high-quality SAR data with decimeter resolution and allow the development of novel approaches in data analysis and information extraction from SAR. In this paper, a review about the abilities and needs of today's very high-resolution airborne SAR sensors is given, based on and summarizing the longtime experience of the German Aerospace Center (DLR) with airborne SAR technology and its applications. A description of the specific requirements of high-resolution airborne data processing is presented, followed by an extensive overview of emerging applications of high-resolution SAR. In many cases, information extraction from high-resolution airborne SAR imagery has achieved a mature level, turning SAR technology more and more into an operational tool. Such abilities, which are today mostly limited to airborne SAR, might become typical in the next generation of spaceborne SAR missions.

In this paper an extension of our “Fast GMTI Algorithm for Traffic Monitoring Based on A Priori Knowledge” [1,2] to an arbitrary number of M receiving (RX) channels is presented. This is done by incorporating Post-Doppler space-time adaptive processing into the processing chain. In contrast to our original dual-channel algorithm this additionally allows for robust estimation of the direction-of-arrival (DOA) angles of the detected signals. As a consequence false detections can be recognized and discarded. In the paper the processing chain is explained and performance estimation results for DLR's multi-channel airborne F-SAR system are presented and discussed.

This paper presents an initial analysis of the possibilities for velocity and acceleration measurement with the Bi-Directional SAR imaging mode (BiDi). It comprises single satellite single path acquisitions as well as a constellation of two satellites. The translational velocity components into azimuth and range directions are simulated. A BiDi approach for measuring the azimuth velocity from one satellite with one receiving channel is proposed. Image examples acquired with the TerraSAR-X (TSX) and TanDEM-X (TDX) satellites show velocity and acceleration effects on ships and the proposed BiDi velocity approach is verified. Interferometric fringes were observed on anchoring ships. A first approach into the understanding of rotational effects was achieved by simulation of rotational fringes.

In this paper, a fast a priori knowledge-based ground moving target indication and parameter estimation algorithm applicable to single- as well as to multichannel synthetic aperture airborne radar data is presented. The algorithm operates directly on range-compressed data. Only the intersection points of the moving vehicle signals with the a priori known road axes, which are mapped into the range-compressed data array, are evaluated. For moving vehicle detection and parameter estimation, basically only a single 1-D fast Fourier transformation has to be performed for each considered road point. Hence, the required computational power is low, and the algorithm is well suited for real-time traffic monitoring applications. The proposed algorithm enables the estimation of the position and velocity vectors of detected moving vehicles independent of the number of channels. A single-channel synthetic aperture radar system may be sufficient in case of fast moving vehicles. The paper includes a detailed performance assessment together with experimental results that demonstrate the applicability in a real-world scenario.

In the paper first ground moving target indication (GMTI) and parameter estimation results obtained with the spaceborne TerraSAR-X/TanDEM-X satellite constellation are presented and discussed. For processing a dual-platform GMTI algorithm developed by the authors was used. This algorithm enables the estimation of the true geographical positions, the velocities and the headings of the detected targets with high accuracy. The algorithm is verified and evaluated using ground truth reference data.

Radar systems operating on high altitude platforms can provide traffic information over wide areas, independent of sunlight illumination and weather conditions. In the paper, a novel a priori knowledge based ground moving target indication (GMTI) and parameter estimation algorithm applicable on single- as well as on multi-channel synthetic aperture radar (SAR) data is presented. Only the intersection points of the moving vehicle signals with the a priori known road axes, which are mapped into the range-compressed data domain, are evaluated. The algorithm needs low computational load and is hence well suited for real-time traffic monitoring applications.

We report about the first X-band spaceborne-airborne bistatic synthetic aperture radar (SAR) experiment, conducted early November 2007, using the German satellite TerraSAR-X as transmitter and the German Aerospace Center's (DLR) new airborne radar system F-SAR as receiver. The importance of the experiment resides in both its pioneering character and its potential to serve as a test bed for the validation of nonstationary bistatic acquisitions, novel calibration and synchronization algorithms, and advanced imaging techniques. Due to the independent operation of the transmitter and receiver, an accurate synchronization procedure was needed during processing to make high-resolution imaging feasible. Precise phase-preserving bistatic focusing can only be achieved if time and phase synchronization exist. The synchronization approach, based on the evaluation of the range histories of several reference targets, was verified through a separate analysis of the range and Doppler contributions. After successful synchronization, nonstationary focusing was performed using a bistatic backprojection algorithm. During the campaign, stand-alone TerraSAR-X monostatic as well as interoperated TerraSAR-X/F-SAR bistatic data sets were recorded. As expected, the bistatic image shows a space-variant behavior in spatial resolution and in signal-to-noise ratio. Due to the selected configuration, the bistatic image outperforms its monostatic counterpart in almost the complete imaged scene. A detailed comparison between monostatic and bistatic images is given, illustrating the complementarity of both measurements in terms of backscatter and Doppler information. The results are of fundamental importance for the development of future nonsynchronized bistatic SAR systems.

Following the success of the first bistatic spaceborne-airborne experiment between TerraSAR-X and F-SAR carried out in November 2007, DLR has performed a second bistatic experiment in July 2008 with new challenging acquisitions. Furthermore, the existing bistatic processing chain has been updated with two significant improvements: a) clock offset synchronisation is now performed without the use of reference targets, and b) SAR imaging is done using a fast focussing technique. The new SAR imaging algorithm, based on the fast factorised backprojection algorithm, has proved very good focussing qualities while dramatically reducing (up to a factor 100 with respect to direct backprojection) the overall computational load. The new processing chain is tested using the image of the first TerraSAR-X experiment. Results of a dualpol acquisition performed during the second TerraSAR-X/F-SAR experiment and showing the first dual-pol bistatic spaceborne-airborne images are also presented in this paper.

In the paper a ground moving target indication (GMTI) algorithm operating on preprocessed range-compressed SAR data is presented. For preprocessing range cell migration correction for stationary targets in most practical cases is sufficient. Moving target signal detection and extraction, adaptive range cell migration correction, position and across as well as along-track velocity estimation is then performed without a priori knowledge by using matched filter banks and the fractional Fourier transform. The proposed algorithm is able to cope with multi-component linear frequency modulated signals as they arise in real traffic scenarios containing a number of moving road vehicles.

Following an original proposal by the authors to the TerraSAR-X (TSX) scientific coordination board, a spaceborne-airborne bistatic experiment was successfully performed early November 2007. TSX was used as transmitter and DLR's new airborne radar system, F-SAR, as receiver; due to the capability of the latter to acquire data quasi-continuously, no echo window synchronisation is needed. Monostatic data were also recorded during the acquisition. This paper includes description and results of the spaceborne-airborne bistatic experiment, with special focus on data processing and image comparison. Given the acquisition scenario, with two-channel sampling and transmitter and receiver clocks operating independently, data processing must necessarily follow a three-step strategy: 1) channel balancing, 2) data synchronisation and 3) bistatic SAR processing. Since neither absolute range nor Doppler references are available in the bistatic data set, synchronisation is done with the help of calibration targets on ground and based on the analysis of the acquired data compared to expected data. Due to the variant nature of the bistatic acquisition and the required precision for the processing, data are processed using a bistatic backprojection approach.