Nach oben pdf Relativistic Effects in Bistatic Synthetic Aperture Radar

Relativistic Effects in Bistatic Synthetic Aperture Radar

Relativistic Effects in Bistatic Synthetic Aperture Radar

The capabilities of bistatic and multistatic SAR missions are accompanied by new challenges regarding radar system imple- mentation, operation, and product generation. Well-known challenges are time and phase synchronization, relative posi- tion sensing, selection of appropriate transmitter and receiver trajectories, joint antenna steering, avoidance of mutual irradi- ation, and bistatic SAR processing [6]–[10]. Up until now, the topics bistatic radar synchronization, relative position sensing, and bistatic SAR processing have been treated almost indepen- dently. In doing so, an important aspect has been neglected. Radar time and phase synchronization are typically performed (and thought of) in a platform-based reference frame, while an earth-centered earth-fixed (ECEF) reference frame is usually employed to specify the platform ephemerides and the bistatic SAR processing equations. In this paper, we will show that the unreflected mixture of different reference frames for bistatic SAR data acquisition and bistatic SAR processing may cause notable localization and phase errors in the focused bistatic SAR images. For most bistatic and multistatic SAR systems, these errors can be well approximated and corrected for by considering the space-time relations between two inertial reference frames as established in Einstein’s special theory of relativity. The predicted phase and localization errors and their dependence on the formation geometry are in good agreement with systematic latitude-dependent DEM offsets that have been observed by evaluating a large number of bistatic TanDEM-X data takes.
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Passive Interferometric Ocean Currents Observation Synthetic Aperture Radar (PICOSAR)

Passive Interferometric Ocean Currents Observation Synthetic Aperture Radar (PICOSAR)

The predicted performance for PICOSAR clearly meets the scientific requirements. In combination with the yet clear and technologically feasible mission concept we yield a strong mission proposal. In addition DLR has leading expertise in all critical aspects of the mission, i.e. SAR, Bistatic SAR and formation flying, and the TanDEM-X heritage is certainly the key for a successful and cost-effective mission implementation. The general mission concept is not restricted to use Sentinel-1 as the transmitter, but could easily be adjusted to combine with other SAR satellites, too. A close collaboration with the Agency operating the transmitting satellite (e.g. the European Space Agency in case of S1) is practically mandatory. Exact knowledge of the transmitter system, modes and acquisitions schemes is required.
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Interferometric Synthetic Aperture Radar (SAR) Missions Employing Formation Flying

Interferometric Synthetic Aperture Radar (SAR) Missions Employing Formation Flying

tion of a global DEM of unprecedented accuracy as well as the demonstration of new bistatic and multistatic SAR techniques and applications. The launch of TanDEM-X is planned for spring 2010, and the global DEM should be available after 3–4 years. • Tandem-L is a proposal for a next-generation single-pass interferometric and fully polarimetric L-band radar mission that systematically monitors dynamic processes in the Earth environmental system using advanced SAR techniques and technologies. A primary goal of Tandem-L is the estimation of above-ground forest biomass with an accuracy of 20% on a global scale. Annual biomass changes will be measured throughout the mission lifetime of 5–7 years as well. In addition, single- pass polarimetric SAR interferometry enables also the measurement of bare soil topography as complementary information to the surface DEM that will be provided by the TanDEM-X mission. This paper is organized as follows. Section II provides an introduction to the basic principles and applications of SAR interferometry. Sections III and IV form the core of the paper and provide an overview of the TanDEM-X and Tandem-L mission designs, respectively. Enabling tech- nologies and techniques are described in detail for both missions. Section V gives an outlook on future interfero- metric and tomographic mission concepts and develop- ments, including multistatic SAR systems. The paper concludes with a short summary in Section VI.
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Geosynchronous synthetic aperture radar: design and applications

Geosynchronous synthetic aperture radar: design and applications

Figure 2-6. Effects of terrain relief on side-looking radar images. Feature (A) illustrates layover effect; features (B), (C) and (D) describe the shadow effect; feature (D) describes the foreshortening effect. (Lillesand and Kiefer, 2000). When the slope facing the antenna is less steep than the line perpendicular to the look angle, layover does not occur (feature D in Figure 2-6) as the response of the base arrives to the antenna earlier than that of the top of the structure. In this case, many areas of the slope appear to have the same distance from the spacecraft. The receiver collects all the backscattered echoes at the same time and interprets them as a small area with a very high backscatter instead of detecting a large area with a smaller backscatter. This effect is called foreshortening. In feature C the front slope is perpendicular to the look angle and it has been foreshortened to zero length.
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Airborne tripwire detection using a synthetic aperture radar

Airborne tripwire detection using a synthetic aperture radar

The measurement system employed is shown in Fig. 1. It consists of an industrial multicopter with an integrated inertial measurement unit (IMU) [14], a real time kinematic global navigation satellite system (RTK GNSS) with a horizontal and a vertical accuracy of ±10 mm and ±20 mm, respec- tively [15], and a bistatic frequency-modulated continuous- wave (FMCW) SAR operating in the frequency range from 1 GHz to 4 GHz [16]. The output data of the RTK GNSS (5 Hz), the multicopter integrated IMU (50 Hz), and the SAR (30 Hz) are time-stamped and stored on a single-board com- puter for offline processing.
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Predictive Quantization for Staggered Synthetic Aperture Radar Systems

Predictive Quantization for Staggered Synthetic Aperture Radar Systems

Synthetic Aperture Radar (SAR) is an active remote sensing technique aimed at mapping and monitoring the Earth surface. Atmospheric phenomena such as clouds and fog do not have significant impact on SAR measurements, allowing this type of systems to perform in any weather condition. Moreover, being an active system (i.e. it provides its own illumination), SAR can perform independently also from solar illumination, while using optical passive sensors this is not possible. SAR systems are usually equipped on satellite or airborne platforms, both offering advantages and disadvantages for different applica- tions. While satellites are more stable and guarantee a longer mission lifespan, airborne platforms can achieve higher resolution and are less expensive. SAR systems can either be monostatic or bistatic if one or two sensors are employed for transmission and reception, respectively. In general, radar systems are microwave imaging sensors characterized by an azimuth resolution which is limited by the antenna dimension. Since the antenna beam width is inversely proportional to its dimension (i.e. size), a smaller antenna will be less directive. Thus, to obtain a sufficient resolution, a longer antenna is necessary. This is the case of Real Aperture Radar (RAR), for which the azimuth resolution is given by
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Sparse Reconstruction Automaton for Synthetic Aperture Radar Tomography

Sparse Reconstruction Automaton for Synthetic Aperture Radar Tomography

We apply LIKES to a stack of 21 real bistatic SAR interferograms. Fig. 5 shows our preliminary results—the first (b) and second layer (c) of elevation estimates, superimposed on mean intensity image (a). The layover effect of roof and façade is obvious to observe at the top of the façade.

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Bathymetry derived from Sentinel-1 Synthetic Aperture Radar

Bathymetry derived from Sentinel-1 Synthetic Aperture Radar

For this investigation, an FFT boxsize of 5.12 km × 5.12 km was used, where the FFT boxes were arranged in an overlapping, gridded pattern offset by 700 m in both directions. With external data from the GEBCO 2014 dataset, the automatically retrieved wave period is 12.31 s. The RMSD between both datasets for this wave period is 12.45 m. Since the investigated depths are up to 90 m, this value is within 15% accuracy. However, the GEBCO dataset used for comparison cannot be consid- ered to contain fully accurate bathymetry in this region. Further possible sources of error that are not incorpo- rated in the calculation are ocean currents, which change the surface wavelengths, and tidal effects changing the water level compared to values in sea charts or datasets.
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Accurate Antenna Characterization for
Wideband Synthetic Aperture Radar Processing

Accurate Antenna Characterization for Wideband Synthetic Aperture Radar Processing

A large sweep bandwidth of the network analyzer signals allows an adequate downrange resolution that facilitates time domain post-processing of the acquired antenna data. Positioner velocity shall allow a high number of frequency increments within the sweep time of the signal analyzer to avoid unwanted range aliasing effects. With this method, the investigated radiating surface is enlarged from the bare antenna to the complete antenna carrier structure. The electromagnetic vectors of all parasitic scatters are superimposed with that of the radiating antenna element. Measurements now include multi-pass effects on the structure and scattering on edges that influence the antenna patterns in amplitude and phase, see figures 3 and 4, with the spherical contour plots of the X-band antenna measurements in a elevation over azimuth coordinate system
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Hurricane Monitoring With Spaceborne Synthetic Aperture Radar

Hurricane Monitoring With Spaceborne Synthetic Aperture Radar

With respect to measurements of sea surface wind field in TCs using scatterometer or SAR, two major sources may limit the accuracy of retrieval for high winds: (1) deficiencies of the Geophysical Model Function (GMF) for high winds, as presented in [ 7 ]. Improvement of GMF, such as CMOD5 [ 8 ] is dedicated for retrieval of high wind using scatterometer or SAR data, has somewhat reduces this error sources for inversion of sea surface wind field in hurricane scale [ 9 ]. However, one still faces the problem of speed ambiguity when applying CMOD5 for retrieving high winds [ 10 ] and saturation or damping of radar signal under severe weather conditions [ 11 ]. (2) Effects of heavy rains on radar signal. Microwave signals are likely to suffer effect of heavy rains which are permanent features in TCs and therefore errors are induced of deriving sea surface wind speed, e.g., studies presented by Quilfen et al. [ 7 ] and Weissman et al. [ 12 ]. Yueh et al. [ 13 ] proposed an updated GMF for retrieval of sea surface wind field considering the rain rate as a parameter, which is applied to the hurricane Floyd with maximum wind speed reaching 60 m/s showing a good agreement NOAA Hurricane Research Division (HRD) wind reanalysis.
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Localising vibrating scatterer phenomena in synthetic aperture radar imagery

Localising vibrating scatterer phenomena in synthetic aperture radar imagery

Modelling vibrating target paired echoes: Cran field University’s GBSAR laboratory [10] operates a rail-based SAR system, the speed of the rail is low and constrained by the hardware. To measure vibrating target artefacts using the system, we present an approach for how the effects from any platform velocity and vibration frequency can be repro- duced if an appropriate synchronisation between the scanner and the target is achieved, in effect resulting in a synthetic platform velocity and corresponding target vibration. In the following sections we for- malise this approach by showing how dependencies on time can be replaced with dependencies on the relative positions of the platform and vibrating target alone.
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System design for geosynchronous synthetic aperture radar missions

System design for geosynchronous synthetic aperture radar missions

In Europe, one of the first published GEO SAR concepts was by Prati et al. [5] in 1998. They described a bistatic passive radar reusing L-band broadcast signals. Such a system could achieve 120-m spatial resolution using an antenna with a diame- ter 4.8 m. The orbit inclination is small (satellite motion of only 25 km from the geostationary position is assumed). However, a long integration time of up to 8 h is required to form a satisfac- tory image. Imaging effects of clutter and partially stable tar- gets, as well as measuring the atmospheric phase screen (APS) are noted. Research on other GEO SAR concepts (mainly con- ventional monostatic) has continued with contributions from Cranfield [6]–[8], Milan [9]–[11], and Barcelona [12], [13] in particular. These recent studies have made significant contribu- tions in the areas of system design and APS estimation/phase compensation. For the low inclination orbits and modest an- tenna sizes, which these authors have assumed, integration times are relatively long, and thus, atmospheric phase com- pensation is needed. There has been particular interest again in applications for short repeat period interferometry related to geohazards.
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Synthetic aperture radar remote sensing for landfill monitoring

Synthetic aperture radar remote sensing for landfill monitoring

4.8 Ascending and Descending Scenes Coregistration The research explores SAR data acquired from both ascending and descending orbits. In some instances, these scenes, or products derived from them, are merged together into a single image. As briefly explained in Section 2.1.2, foreshortening, layover and shadow effects in the radar slant direction can have a drastic impact on an image especially in the presence of steep topography. Due to the different points of view of the ascending and descending scenes, these distortion effects are produced in different directions and consequently the combination of such data sets must be carried out with particular attention. This matter has not been significantly investigated in the literature. The fusion of multi-angle ERS-1 and 2 tandem data has been applied in previous research (Sansosti et al., 1999; Coltelli et al., 1998 and Carrasco et al., 1997) to improve the accuracy of digital elevation models and to detect ground displacements in three dimensions as opposed to a single-orbit-direction tandem pair which can instead only detect displacements in the SAR slant direction. More specifically, Werner et al. (2002) discuss issues related to SAR geocoding and multi-sensor image registration. They use the satellite orbit and local terrain information to reduce coregistration errors. They also select recognisable image features as ground control points as an additional aid. Their analysis highlights the problem of selecting certain persistent scatterers which might not have the same position in the ascending and descending images due to their height (e.g. high buildings). The utilisation of SAR images with different acquisition geometries has also been applied by Dell’Acqua et al. (2003). They manually coregister three simulated SAR images with 3 m ground resolution and generated each with a different viewing geometry. Dell’Acqua et al. (2003) try to extract an accurate map of the road system of the city of Pavia in northern Italy and do not use the images for a direct multi-band classification because the same buildings have a difference appearance in the two images.
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Relativistic Effects in Bistatic SAR Processing and System Synchronization

Relativistic Effects in Bistatic SAR Processing and System Synchronization

Bistatic and multistatic SAR systems operate with dis- tinct transmit and receive antennas that are mounted on separate platforms. The spatial separation enables new radar imaging modes and is well suited to in- crease the capability, flexibility and performance of SAR systems and missions, thereby allowing for the acquisition of novel information products [1]. A prominent example is the TanDEM-X mission where a global DEM is acquired with two X-band SAR sat- ellites flying in close formation [2]. The standard ac- quisition mode in TanDEM-X is the so-called bistatic mode where one satellite illuminates the scene with a sequence of radar pulses and both satellites receive the scattered signal echoes from the ground. The si- multaneous reception by two receivers makes not only efficient use of the transmitted signal energy, but minimizes also the impact of temporal decorrelation.
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Position acquisition for a multicopter-based synthetic aperture radar

Position acquisition for a multicopter-based synthetic aperture radar

The performance of the position acquisition systems is particularly relevant for dynamic pro- cesses in 3D space. The systems are compared on the basis of the quality of SAR images using a simple measurement scenario shown in Fig. 3 and realistic trajectories as shown in Fig. 4. In order to avoid interferences from buildings or trees (multipath effects), the measurements were carried out on a large open area. The minimum and maximum distance between the total station and the prism was 4 m and 35 m, respectively. As reference 20 reflectors have been placed on the surface as shown in Fig. 3. The UAS was manually steered to perform 12 stripmap
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Monitoring permafrost environments with Synthetic Aperture Radar (SAR) sensors

Monitoring permafrost environments with Synthetic Aperture Radar (SAR) sensors

The DInSAR and advanced time-series InSAR methods have been tested recently for monitoring permafrost thaw subsidence and frost heave [18], [20]–[26]. One of the main limitations of DInSAR applications is the signal loss caused by insuffi- cient interferometric coherence, which describes the degree of correlation between two SAR observations [17]. This feature indicates the quality of the DInSAR results. The loss of phase coherence can be explained by a number of reasons, including thermal noise from the antenna, large interferometric baseline, topographic effects, and misregistration between SAR images, and atmospheric effects; however, it can also be caused by land surface changes that occur between two SAR acquisitions [27]. Permafrost regions are usually difficult to access because of their remoteness and harsh environmental conditions; therefore, the availability of ground-truth data for these regions is lim- ited. Although quantitatively validating DInSAR displacements over permafrost is difficult, several efforts have been reported. Short et al. first used in situ thaw tubes [21], [28], [29] to obtain ground observation data to evaluate RADARSAT-2 DInSAR products at the Iqaluit Airport, Baffin Island, Canada [21]. The thaw/frost tube is a classical method used to record thaw set- tlements and frost heave in permafrost areas. In this method, a tube is anchored vertically in permafrost, a metal grill is placed on the ground surface, and a scriber scratches the tube when it moves with the upward and downward movement of the metal grill [21], [28], [29]. In the work undertaken by Short et al., DInSAR-derived seasonal ground displacement patterns aligned well with in situ measurements. In dry areas, the data showed subcentimeter consistency. However, in low-lying wet areas, the DInSAR stack significantly underestimated the true thawing settlement because the combination of high phase gra- dients and poor coherence over intermittently flooded surfaces increases the difficulty of preserving reliable phase measure- ments [21]. Subject to saturation and flooding, changes in soil moisture between two radar acquisitions can also influence DIn- SAR displacements [10], [30].
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High Precision Antenna Characterisation for Broadband Synthetic Aperture Radar Processing

High Precision Antenna Characterisation for Broadband Synthetic Aperture Radar Processing

A large sweep bandwidth of the network analyzer signals allows an adequate downrange resolution that facilitates time domain post-processing of the acquired antenna data. Posi- tioner velocity must allow for a high number of frequency increments within the sweep time of the signal analyzer to avoid unwanted range aliasing effects. With this method, the investigated radiating surface is enlarged from the bare antenna to the complete antenna carrier structure. The electromagnetic vectors of all parasitic scatters are superimposed with that of the radiating antenna element. Measurements now include multi-path effects on the structure and scattering on edges that influence the antenna patterns in amplitude and phase.
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Orbital Effects in Spaceborne Synthetic Aperture Radar Interferometry

Orbital Effects in Spaceborne Synthetic Aperture Radar Interferometry

Radarsat-1 The design of the first Radarsat satellite did not consider InSAR applications at all. In contrast to ERS-1/2, no altimeter is part of the payload either, which would have implied more exi- gent requirements for orbit determination. As a consequence, Radarsat-1 operates in a 5 km wide orbital tube, is not yaw-steered, and the orbit is determined by less precise ground-based radar tracking (Vachon et al., 1995; Geudtner et al., 1998; S. Cˆ ot´ e, CSA, pers. comm., 2012). There has been only one validation campaign that involved ground-based transponders and the detection of their responses in SAR images (CSA, 2010). Due to the poor accuracy of these measurements, only an upper bound for the actual orbit error of a few tens of metres can safely be inferred. Pepe et al. (2011) give a heuristic estimate of the orbit accuracy based on InSAR applications, which is ”on the order of some meters”. Besides, the rel- atively loose orbit control, which implies less frequent calibration maneuvers, may also explain occasional temporal correlations of orbit errors, which have been observed by (Hooper et al., 2007, p. 11).
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Motion Compensation for Near-Range Synthetic Aperture Radar Applications

Motion Compensation for Near-Range Synthetic Aperture Radar Applications

To investigate the influences of motion errors or evaluate the performances of moti- on compensation algorithms, realistic and complete motion parameters of the vehicle dynamics are required. One way to obtain the motion parameters is to purchase them from professional vehicle dynamics measurements company, such as Drivability Tes- ting Alliance[135]. However, the exact measurement of the vehicle performance in dri- ving tests is a nontrivial problem. As depicted in Figure A.1, high accuracy dynamics measurements of ground vehicle need various kinds of sensors, such as acceleration sensor, fiber optic gyroscopes, wheel vector sensor, laser sensor, GPS-based speed and position sensor, etc., thereby being expensive to carry out. Furthermore, such measu- rements are specific for a certain vehicle under certain circumstances instead of the whole spectrum of the vehicle dynamics, which is preferred for a guidance research. On the other hand, in the standards ISO 2631 [114] and VDI 2057 [136], the guidelines of the effects of exposure to vibration on humans have been presented, which cover a very wide frequency bandwidth and amplitude range. Therefore it is reasonable to use such standards for designing motion parameters for the simulations with regard to evaluating the influences of motion errors and performances of the corresponding motion compensation algorithms.
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Synthetic Aperture Weather Radar

Synthetic Aperture Weather Radar

is very sensitive to multiplicative noise, phase error, the image is both shifted and smeared after the SAR post filter without phase-error mitigation. For better understanding, a simple block diagram of a SAR system contami- nated by phase error/multiplicative noise and an example regarding effects of this phase noise on a single target as well are shown in figure (2.11) and figure (2.12) respectively. In figure (2.12), phase-noise-free raw samples, processed samples of contaminated data and phase-error-corrected samples are com- pared together to demonstrate the impacts of the low-order (narrow-band) phase noise with the emphasis on the first and second moments of the FT. As anticipated, the image is shifted and also smeared ( the resolution is degraded considerably). You may find a few other examples of the low-order effects of phase noise on true images such as those extracted by DLR-airborne SAR system, in Danklmayer et al. (2005).
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