Although drone synthetic aperture radar (SAR) is still a relatively new research area, several research groups have proposed hardware setups and experimentally demonstrated various applications. A low-cost, high-resolution frequency-modulated continuous wave (FMCW) SAR drone operating at 77 GHz and 1 GHz bandwidth without using a dedicated inertial navigation system (INS) or global navigation satellite system (GNSS) was presented by Bekar et al. [1] . For short ranges, a cross-range resolution of 2 cm was achieved. A demonstration of simultaneous localization and imaging with their FUSAR-Ku drone is given by Luomei et al. [2] . With simultaneously centimeter-to-decimeter 3D self-positioning accuracy, an azimuth resolution of 10 cm was realized.

The miniaturization of frequency-modulated continuous-wave synthetic aperture radar systems has become increasingly important in recent years. In this paper, we present the ongoing improvements on our SAR drone hardware setup, with special focus on the integration of moving baseline aided navigation data. Furthermore, an experiment investigates the detection of covered reflectors, determines the attenuation factor, and demonstrates the advantages of moving baseline supported processing.

Digital surface models (DSMs) are sets of elevation data of the Earth's surface useful for urban studies representing height estimation of buildings. They can be derived from a set of synthetic aperture radar (SAR) images acquired in an interferometric (InSAR) or tomographic configuration (TomoSAR). More informative DSMs can be obtained by exploiting polarimetric TomoSAR data acquired in spotlight mode with a circular flight path. This configuration, referred to as polarimetric holographic SAR (HoloSAR) allows one to obtain DSMs with a high point den-sity and 360-degree information of the objects. In this work, we present steps for deriving DSMs with polarimetric HoloSAR data by means of a time domain back-projection algorithm (TDBP) and sub-aperture processing. Performance analysis showed that the resulting DSM is more similar to an equivalent LiDAR-based reference DSM in comparison to those derived with single-aspect or single-polarization data.

Conventional synthetic aperture radar (SAR) processing can be adapted to obtain 2-D high-resolution images of moving targets. The resulting images, however, suffer from layover effects, therefore making their interpretation more challenging. This problem can be dealt with by combining radar imaging with interferometry, which allows for 3-D target reconstruction. This article presents a 3-D imaging technique built upon the well-known global backprojection (GBP) algorithm, which has been adapted to focus moving targets rather than the stationary scene. The proposed algorithm is tested on real datasets acquired with a circular acquisition geometry. Using a nonlinear flight-path allows to monitor the target from multiple viewing angles, therefore overcoming the limitations that are common to stripmap SAR (e.g., shadowing effects, geometrically adverse cases, and so on). The obtained results, which were compared with independent ground measurements, demonstrate the accuracy of the proposed method.

In the past few years frequency-modulated continuous-wave synthetic aperture radar enabled the miniaturizing of carrier systems. Small aircraft as well as UAV/drones are nowadays capable of producing electro-magnetic radar images of the earth surface. In this paper, we present our drone hardware setup, its operational mode including a quasi real-time downlink and produced images via time domain back projection. Furthermore, using the airborne system MIRANDA-35 we present preliminary results of a new autofocusing technique targeting modern UAV SAR applications. The algorithm is constructed as an optimal path problem.

Frequency-modulated continuous-wave (FMCW) synthetic aperture radar (SAR) systems represent an effective solution for observing the earth surface. Although they have recently grown in popularity, FMCW SAR raw signals are sometimes not open access, thus preventing the user from performing those processing techniques designed in the raw domain. In this paper, we develop a data inversion technique which converts the input single look complex (SLC) image back into the original raw domain. A real data-set, acquired by the Fraunhofer (FHR) MIRANDA-35 sensor, is used in order to validate the implemented algorithm.

Multi-platform campaigns open up new opportunities and allow a comparison and analysis of SAR signatures of the same area at different frequency bands and incident angles. In this paper, we make use of two different sensor systems: DLR's pulsed F-SAR operating at X-, C- and L-band, and FHR's frequency-modulated continuous-wave (FMCW) MIRANDA35 operating at Ka-band. Main goals of the campaign include the comparison of high-resolution polarimetric signatures at different frequency bands, change detection, synchronized acquisition of ground moving targets and air-moving target indication (AMTI) experiments. We present the planning and implementation of the campaign and first results of the data processing.

Change detection from multi-sensoral SAR-MS datasets is useful for improving the performance by adding data from another modality or for increasing the number of available times. Few methods with two modalities have been investigated though, and often only for low-resolution data. Here, we approach change detection of decimeter-resolution data from an airborne platform. Geometric differences are mitigated by generating a point cloud from optical images, focusing the SAR on it and projecting this into camera view. A joint segmentation of the two datasets is followed by a change detection based on Iterated Conditional Modes (ICM) and Dempster-Shafer theory (DS) to mark changes. An experiment with moving cars is conducted to confirm the method's applicability.

Technological advances in frequency-modulated continuous-wave (FMCW) synthetic aperture radar (SAR) and the associated miniaturization and energy efficiency make it increasingly possible to transfer SAR systems from traditional airborne platforms to small UAVs. An important factor to successfully achieve high-quality imaging from SAR systems mounted on small drones is the precise knowledge of the platform's navigation data in best case by avoiding the use of an expensive and heavy inertial measurement unit (IMU). In this paper, we test different concepts and discuss the impact on SAR image quality using FHR's FMCW MIRANDA35 sensor. To compare several methods simultaneously on one platform and to have an IMU as reference, first preparatory steps were carried out on an airborne platform. Specifically, we present and evaluate solutions based on SAR autofocus, moving baseline differential GPS and optical structure-from-motion-based localization. SAR autofocus shows the best performance in our preliminary investigations.

Frequency-modulated continuous-wave (FMCW) synthetic aperture radar (SAR) is now a commonly adopted solution for producing high-resolution electromagnetic images of the earth surface. However, many SAR applications, such as detection or imaging of moving targets, have not yet been thoroughly researched for FMCW scenarios. This paper presents a refocusing technique that works in the wavenumber domain and is based on the reference spectrum of a point-like moving target. The method is illustrated using simulated experiments, and is further tested by processing real data collected by the Ka-band MIRANDA-35 sensor. The obtained results are validated and compared with independent ground measurements, thus demonstrating the accuracy of the presented method.

Information on 3-D structure expands the scope of change detection applications, for example, in urban studies, human activity, and forest monitoring. Current change detection methods do not fully consider the specifics of SAR data or the properties of the corresponding image focusing techniques. We propose a three-stage method complementing the properties of 2-D and 3-D very high-resolution (VHR) synthetic aperture radar imagery to improve the performance of 2-D only approaches. The method takes advantage of back-projection tomography to ease translation of the 2-D location of the targets into their corresponding 3-D location and vice versa. Detection of changes caused by objects with a small vertical extent is based on the corresponding backscatter difference, while changes caused by objects with a large vertical extent are detected with both backscatter and height difference information combined in a conditional random field. Using multitemporal images, the kappa coefficient improved by a factor of two in comparison with traditional schemes.

Frequency-modulated continuous-wave (FMCW) synthetic aperture radar (SAR) are nowadays frequently exploited for producing electro-magnetic images of the earth surface. In this paper, we present first ground moving target indication (GMTI) results of the 2016 campaign with the FMCW airborne system MIRANDA-35, and quantify the overall performances. More precisely, two specific operating modes, namely the along-track interferometry (ATI) and exo-clutter processing (EXO), are discussed in more detail. The system proved to be capable of providing GMTI products with reliable detection rates and acceptable false alarm rates.

We present a Ground-Moving Target Indication (GMTI) approach from aerial oblique RGB imagery in decimeter resolution. To detect moving targets, values of overlapping images are first projected on a 3-D point cloud, then back into one common image geometry and finally compared in their intensity values. False alarms are reduced by checking against a depth gradient. The performance of the method was assessed in an experiment on a rural road. The results show that GMTI is possible from aerial RGB imagery even without an explicit object detection, but false alarms caused by inaccuracies of the 3-D model and the projection parameters need to be considered.

Change detection from multitemporal synthetic aperture radar (SAR) images enables mapping applications for earth environmental observation, human activity monitoring, and urban studies. We expand the use of SAR data beyond single-look processing to include the spatial response of targets. This information is derived from a multisquint framework similar to beamforming. To preserve changes detected at nominal resolution, a three-stage change detector exploiting single-look and multisquint processing mode is proposed to mitigate false alarms caused by image artifacts typically found in high-resolution SAR imagery and urban scenarios. After applying the proposed method to multitemporal images, the false alarm rate was reduced by a factor 3, while preserving 95% of the detection rate offered by traditional schemes.

Detecting and tracking moving targets in synthetic aperture radar (SAR) data is a challenging task, demanding state-of-the-art processing methods and advanced SAR systems. Current approaches concentrate on the problem of either endo-clutter moving target tracking or exo-clutter moving target tracking, neglecting the advantages of a joint tracking framework. We present an approach relying on a combined exo- and endo-clutter processing scheme using SAR data with a high pulse repetition frequency. The main processing chain is subdivided into four major steps: 1) focusing of temporal and spatial overlapping SAR images; 2) extracting image statistics for each of these subaperture images in the endo- and exo-clutter domains; 3) subsequent tracking of both endo- and exo-clutter observations using multitarget unscented Kalman filtering; and 4) calculating real-world speeds and positions from the SAR image space coordinates using a road network. The results of this approach are validated and compared with ground-based measurements, and it is found that 100% of the vehicles were detected correctly with an accuracy in speed of 0.02 ± 0.31 m/s and an average tracking time of ~28 s.

Moving targets in Synthetic Aperture Radar (SAR) data impose several challenges for SAR processing. In full-aperture SAR images, moving objects appear smeared and/or displaced. In previous studies, we showed that a combination of sub-aperture processing, feature extraction, unscented Kalman filtering and multi-target hypotheses can reliably detect and track moving objects. In this paper, we present some extensions to the tracking algorithm itself, show results on a newly acquired data set and demonstrate how the estimated trajectories of the moving objects can be utilized to refocus the originally smeared objects.

Methods for moving target tracking in single- and multichannel synthetic aperture radar (SAR) are presented and evaluated using data from the F-SAR system. An approach based on temporal and spatial overlapping SAR images, image statistics, and multitarget unscented Kalman filtering showed promising results for a single-channel linear SAR experiment. Here, we extend this approach to integrate along-track multichannel data, combine single- and multichannel extraction methods, and verify our results on nonlinear test data on all the different extraction methods. The results are validated and compared with ground-based measurements on a highway section, demonstrating the accuracy of the presented method.

In this contribution, a car-borne SAR and InSAR experiment is described. The slope of a valley was imaged by means of a single-pass InSAR system mounted on a car driving on roads along the bottom of the valley. The GAMMA portable radar interferometer GPRI-II hardware with a modified antenna configuration was used for data acquisition. The experimental setup (1), SAR imagery focused along a slightly curved sensor trajectory (2), and first interferometric results (3) obtained using this configuration are presented.

A method for moving target tracking using single-channel Synthetic Aperture Radar (SAR) is presented. The approach is based on sub-aperture SAR processing, image statistics and multitarget unscented Kalman filtering. It is capable of robustly detecting and tracking moving objects over time, providing information about their trajectories in the image space while illuminated by the radar beam. We have successfully applied the method on experimental data sets using MiSAR and F-SAR to accurately characterize the movement of vehicles on highway sections in the image space.

A novel method for moving-target tracking using single-channel synthetic aperture radar (SAR) with a large antenna beamwidth is introduced and evaluated using a field experiment and real SAR data. The presented approach is based on subaperture SAR processing, image statistics, and multitarget unscented Kalman filtering. The method is capable of robustly detecting and tracking moving objects over time, providing information not only about the existence of moving targets but also about their trajectories in the image space while illuminated by the radar beam. We have successfully applied the method on an experimental data set using miniature SAR to accurately characterize the movement of vehicles on a highway section in the radar image space.

First experimental results with a low-frequency, ultra widebad (UWB) radar for estimating the height of glacier beds are illustrated. We use a 3-dimensional Time-Domain Back-Projection (TDBP) algorithm which incorporates the influence of the refractivity of ice to reconstruct the glacier bed of the Aletsch Glacier in the Swiss Alps using several CARABAS data sets. As the results indicate, the proposed method underlines the ability of low-frequency Synthetic Aperture Radar (SAR) to penetrate into glacier ice and thus, to map glacier volumes on a large scale even with only few, suboptimal data acquisitions.

Bistatic SAR experiments are in focus in recent years. We will present first results of an airborne bistatic experiment conducted 2007 in Switzerland with the Swedish ultrawideband sensor CARABAS, operating at 28-73 MHz (UWB). The acquired bistatic data include HH and HV polarization with different bistatic elevation angles, various transmitting flight tracks - including non-linear tracks - and fixed receiving antennas (horizontal and vertical) installed on a mountain top. We will place an emphasis on the processing, improving and evaluating of the data and give an overview of future applications which are made feasible by this bistatic dataset.