Advanced multichannel spaceborne synthetic aperture radar (SAR) systems with multiple elevation beams allow to obtain high-resolution and wide-swath SAR images. However, the degradation of the SAR image quality due to range ambiguities is still an issue. In this paper, the range-ambiguity problem is analytically modeled and solved by employing a range-time and Doppler-frequency dependent mixing matrix which describes the linear superposition of multiple mutually range-ambiguous radar echoes. The overall framework can be regarded as a generalization of the cocktail party problem where each listener has to separate one speech signal out of a linear superposition of multiple voices. For the first time, the two-dimensional dependence of the antenna radiation pattern in the elevation and azimuth directions is considered by accounting for both the real-time beamsteering in elevation and the systematic variations of the mixing coefficients with Doppler frequency. A novel solution, based on higher-order blind source separation, is presented. The performance of the proposed method is numerically analyzed, with reference to an array-fed reflector antenna SAR system, by simulating a realistic acquisition scenario. To this aim, real SAR data and the actual antenna patterns of the Tandem-L mission proposal are considered.

This paper presents our new SAR processing framework known as PRISM (Processor for Interferometric SAR Missions). This flexible approach allows the efficient and accurate processing of SAR data independent of the sensor and the acquisition mode. The two main PRISM components (the focusing and the interferometric chains) are described in this paper together with the philosophy of the software architecture. Experimental results are presented and discussed based on the impulse response function analysis of simulated data as well as the focusing and interferometric results using real TerraSAR-X 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).

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.