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. 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  and VDI 2057 , 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 motioncompensation algorithms.
3 Intra-Pulse Beamsteering in Elevation One example for multidimensional waveform encoding is intra- pulse beam steering in elevation. This enables an illumination of a wide image swath with a sequence of narrow and high gain antenna beams. Such a staggered illumination is in some sense similar to the traditional ScanSAR mode, with the important difference that each transmitted pulse illuminates now not only one but all sub-swaths simultaneously. The illumination se- quence within each Tx pulse can in principle be arranged in any order. An interesting opportunity arises if we start from far range illumination and proceed consecutively to nearrange as illustrated in Fig. 4. As a result, the radar echoes from different sub-swaths will overlap in the receiver as shown in Fig. 4 on the upper right. The overall receiving window can hence be shortened, thereby reducing the amount of data to be recorded and stored on the satellite without the necessity for real-time on-board processing as in the SCORE process of the HRWS system. The temporal overlap of the radar echoes from the dif- ferent sub-swaths is then resolved in the spatial domain by digi- tal beamforming on receive. This a posteriori processing can be performed off-line on the ground, which has the further advan- tage that no information about the spatial structure of the re- corded radar data will be lost, thereby enabling e.g. a suppres- sion of directional interferences or jamming signals and avoid- ing the mountain clipping problem of the real-time SCORE technique as discussed in .
The selection of the spatiotemporal excitation coeffi- cients for the individual Tx apertures could even be made adaptive by evaluating the recorded samples from previous signal returns (cf. Fig. 3). By this, a closed loop will be formed between the radar sensor and its environment, which allows for a maximization of the information that can be derived about the imaged scene for a given RF power budget. In analogy to the information theoretic modeling of multiple-input multiple-output (MIMO) communication systems, such an optimization could then be regarded as maximizing the mutual information be- tween the recorded radar signals and the scatterer distribu- tion on the ground, thereby making optimum use of the channel capacity provided by the multiple antenna Tx/Rx radar system. For illustration, one may consider the simple case of an automatic compensation of angular variations in the received Rx power being caused by, e.g., range differ- ences, inhomogeneous atmospheric RF signal attenuation, and/or spatial variations in the first-order scattering statis- tics of the imaged scene.
the receive array height. In this system operation, a severe nonuniform sampling will not occur due to the narrow PRF range. In the case that a wide range of PRF is considered, the degree of nonuniform sampling will be significant and reach the maximum at the highest PRF. In the designed system, it is assumed that all receive channels have the identical noise figure of 3.75 dB and a system loss of 3 dB. The required peak and average power of this system are higher than those of the TerraSAR-X system (2-kW peak) in order to achieve the desired SNR over the 100-km swath width. As a rule, the sampling frequency of 275 MHz includes a 10% guard band, and then, the pulse length of 150 μs leads to 41 250 subcar- riers with the 6.67-kHz subcarrier spacing. In this example, we assume that the ICI due to instantaneous Doppler shift is compensated by the method introduced in . Regarding the undersampling for HRWS SAR imaging in the MIMO SAR, the reconstruction algorithm  recovers the original Doppler spectrum prior to the Doppler compensation. This approach has been used for frequency-modulated continuous-wave (FMCW) SAR with DBF in  and also for the very high resolution SAR data processing . Therefore, the ICI issue is not included in the performance estimation in Section VI-A. To improve the computation speed of DFT/inverse DFT, one can select a number of subcarriers that are equal to a power of two. This MIMO SAR antenna is composed of six panels in azimuth and 42 receive subarrays in elevation in each panel. The receive subarray consists of three X-band radiators in elevation and azimuth, respectively. Fig. 12 depicts the MIMO SAR antenna configuration and geometric parameters in this example design.
System Design for Geosynchronous SyntheticApertureRadar Missions
Stephen Hobbs, Cathryn Mitchell, Biagio Forte, Rachel Holley, Member, IEEE, Boris Snapir, and Philip Whittaker
Abstract—Geosynchronous syntheticapertureradar (GEO SAR) has been studied for several decades but has not yet been implemented. This paper provides an overview of mission design, describing significant constraints (atmosphere, orbit, temporal stability of the surface and atmosphere, measurement physics, and radar performance) and then uses these to propose an approach to initial system design. The methodology encompasses all GEO SAR mission concepts proposed to date. Important classifications of missions are: 1) those that require atmospheric phase com- pensation to achieve their design spatial resolution; and 2) those that achieve full spatial resolution without phase compensation. Means of estimating the atmospheric phase screen are noted, including a novel measurement of the mean rate of change of the atmospheric phase delay, which GEO SAR enables. Candidate mission concepts are described. It seems likely that GEO SAR will be feasible in a wide range of situations, although extreme weather and unstable surfaces (e.g., water, tall vegetation) prevent 100% coverage. GEO SAR offers an exciting imaging capability that powerfully complements existing systems.
The relativistic phase and time offsets from this paper are not only of high importance for DEM generation with a formation flying SAR cross-track interferometer. Formations with multiple satellites have also been suggested for a wide range of further remote sensing applications, ranging from along-track interferometry for moving object and ocean current measurements over sparse aperture ambiguity suppression and super resolution for enhanced high-resolution wide-swath SAR imaging up to single-pass SAR tomography for vertical struc- ture measurements –. Due consideration of relativistic effects from varying along-track baselines is again of essential importance for these advanced bistatic and multistatic SAR systems to avoid mutual range and phase offsets between the received SAR signals. The phase accuracy requirements for the combination of the different receiver signals are typically in the order of 1° or a few degrees. For comparison, an along-track baseline of 100 m causes in an X-band system a relativistic phase shift in the order of several tens of degrees. Future multistatic SAR satellite missions should therefore take into account relativistic effects in the design of the radar synchronization system and/or the SAR processor to avoid a possible performance loss.
SAR sensors commonly use wideband chirp signals to illuminate targets perpendicular to the moving direction of the sensor platform. With the long transmit pulses in range and the wide antenna beams in cross-range direction, the received raw data is initially defocused. Refocusing is done in a SAR processor that uses the exact knowledge of the system transfer function and the platform motion. With the SAR principle that is based on a coherent system, the parameters must be available as complex numbers i.e. in magnitude and phase. The radar antennas are anisotropic as a function of spatial direction and demand for a three dimensional characterization in an azimuth over elevation coordinate system over frequency. The F-SAR processer that is as well developed at the Microwaves and Radar Institute allows 3D antenna pattern correction. Compensating for the antenna is a computationally intensive step in SAR processing that requires precise, three dimensional antenna measurements.
Radar images contain quite different information than images obtained from optical or infrared sensors. While in the optical range mainly molecular resonances on the ob- ject surfaces are responsible for the characteristic object reflectivity, in the microwave region, dielectric and geom- etrical properties become relevant for the backscattering. Radar images therefore emphasize the relief and morpho- logical structure of the observed terrain as well as changes in the ground conductivity, for example, caused by differ- ences in soil moisture. Because of the sensitivity to dielec- tric properties, SAR images, in principle, also can provide information about the condition of vegetation, an impor- tant fact for agricultural and forestry applications. Another important feature of SAR data results from the propagation characteristics of microwaves. Microwaves are capable of penetrating into vegetation and even the ground up to a certain depth . The penetration capabilities depend on the wavelength as well as on the complex dielectric con- stants, conductivities, and densities of the observed tar- gets. Shorter wavelengths, like the X-band (3 cm), show typically a high attenuation and are mainly backscattered on the surface or on the top of the vegetation. Conse- quently, at these wavelengths, information about this layer is mainly collected. Longer wavelengths, like L- and P-band (25 cm and 100 cm, respectively), normally penetrate deep into vegetation, snow and ice, and often also into the ground. The backscattering then contains contributions from the entire volume.
We propose a novel physics-based approach which prom- ises to provide a generic approach to clutter modelling suit- able for a wide range of incidence angles and wavelengths. It is based on observations of the true motion of vegetation (wheat, in the first case) in natural wind. These are combined with a focussing algorithm to assess how broadly the scattered power is smeared in azimuth across the image. Parametrisa- tions of the azimuth spread for a particular class of vegetation (wheat, representing short crops) are derived to allow image simulation for a range of landscapes and weather conditions. We expect that similar parametrisation could be developed for other landcover classes, for example rough water, long crops and trees / forest (probably the most important class needed to represent real landscapes).
The SAR processor has to handle low-frequency ultra wideband (UWB) FMCW radar data obtained from a non- linear flight path in the near and far field of a wide-beamwidth antenna. Regarding frequency domain and time domain algo- rithms, back-projection may be the most suitable algorithm to fulfil all the demands at the expense of computing time – . The major advantages of this algorithm are the high image quality and the integrated motioncompensation.
In the maritime sector, SAR is currently being used for the detections of sea state, wind, oil spills, sea ice, icebergs and ships. These detection algorithms have been developed to run automatically in the receiving station to provide results in Near Real Time (NRT). Applications are the improvement of wave and wind models by providing data across large areas, the improvement of maritime domain awareness, and the support of vessels travelling through ice-infested waters.
The German X-band SAR TS-X was launched successfully on 15 June 2007 from Baikonur, Kazakhstan. The satellite is in a near-polar orbit around the Earth, at an altitude of 514 km. Using its active radar antenna, TS-X is able to produce image data with a resolution down to one meter, independent of weather conditions and daylight. It has been fully operational since January 7, 2008. Main technical parameters of TS-X are briefed in Table 6.1 . The detailed information of TS-X mission, design, as well as ground segment is available in [ 16 , 17 ]. Figure 6.1 illuminates three different imaging modes of TS-X, i.e., Spotlight, Stripmap and ScanSAR modes. For both Stripmap and ScanSAR modes, the radar beam can be electronically tilted within a range of 20–45 ◦ perpendicular to the flight direction without having to move the satellite itself. For Spotlight mode, the radar beam can be further tilted to 55 ◦ . Scenes sizes and resolutions of the three imaging modes of TS-X are listed in Table 6.2 .
We found that more than half of the non-tidal wetland studies (55%) were conducted for the mapping of flooded vegetation. Riverine floodplains have important ecological functions for water storage, biodiversity, and agricultural use [ 124 ]. Mapping spatial and temporal patterns of floodplain vegetation as well as monitoring seasonal dynamics of vegetation in large river systems is an important task. Here, the Amazon floodplain is one of the most intensively investigated regions, for example in a study by Forsberg et al. [ 125 ] who used a mosaic of JERS-1 L-band SAR images to investigate the influence of tectonic faults on wetland distributions in central Amazon lowland. A large-scale analysis on wetland extent for a study area of more than 1.7 million square kilometers of the Central Amazon Basin was performed by Hess et al. [ 126 ]. In their study they presented a dual-season mapping of wetland inundation and vegetation under both low-water and high-water conditions at a 100-m resolution for the Central Amazon Basin using mosaics of HH-polarized L-band JERS-1 SAR images that were taken as part of the Global Rain Forest Mapping (GRFM) project. In another approach, spatial analyses on vegetation distribution and flood dynamics in the Amazon floodplain was performed by [ 127 ] using multi-temporal JERS-1 SAR data. Further on, Costa [ 128 ] used C-band RADARSAT and L-band JERS-1 data to map zonation of vegetation communities in the Amazon floodplain. Costa’s analysis showed that the backscatter values of both the C- and L-band data were lowest in periods of minimal water level, and secondly, JERS-1 data showed a larger dynamic range of backscatter in response to the ground cover. In addition, vegetation structure and inundation patterns derived from ALOS/PALSAR were combined to characterize major vegetation types in the Central Amazon floodplain [ 129 ]. In another study, Sartori et al. [ 130 ] extracted polarimetric radar attributes from fully polarimetric ALOS/PALSAR data to discriminate aquatic plants in the Amazon floodplain wetlands, and applied a rule-based classification to map macrophyte species with an overall accuracy of 0.87 [ 130 ]. An object-based image analysis and decision tree classification for the mapping of vegetation in a late Quaternary landform in the Amazonian wetlands was performed by Cordeiro and Rossetti [ 131 ]. In South America, L-band studies were also carried out by Evans et al. [ 132 ], Evans and Costa [ 133 ], and Evans et al. [ 134 ] who performed object-based image analysis using dual season and dual polarization L-band ALOS/PALSAR and C-band RADARSAT-2 data to map ecosystems of the Brazilian Pantanal wetlands showing the spatial distribution of terrestrial and aquatic habitats.
Index Terms—GeoSAR, Geosynchronous SAR, SAR Perfor- mance Estimation Method
I. I NTRODUCTION
The idea of a Geosynchronous SAR (GeoSAR) is not new  and many types of mission have been proposed in the last twenty years: some monostatic –, other bistatic – and even multistatic . Despite all these studies, no mission has flown yet or has even gone further than the phase-0. Many aspects have been investigated, such as the atmospheric corruption of the image , its possible compensation  and the interferometry applications . But there is still uncertainty about the performance achievable on non-static target areas. Such uncertainty could be particularly important for those mission concepts –, – that are characterised by a low azimuth speed, and which use integration time from minutes to hours.
Although conceived for experimental purposes, the pro- posed demonstrator is conceived for a standard production process as it employs a standard high density intercon- nect (HDI) technology. Custom solutions have been devised and tested to adapt the HDI process to the complex stack-up necessary to implement the proposed tile. The main challenge was to design a low-cost SAR tile at the highest limit of com- plexity for the proposed technology. The experimental results demonstrate the validity of the overall approach and the good performance of the whole RF downconversion chain, from the antennas toward the IF output channels. Indeed, the results of the integrated tile are well matched with the behavior of the single blocks individually tested in . A conversion gain of about 35 and 30 dB was demonstrated in the X-band and Ka-band, respectively. Thanks to its compactness, the proposed RF-board tile can be considered an example of a key building block enabling a new range of EO applications based on small satellite platforms.
SAR interferometry exploits the phase measurements to infer differential range and range change in two or more SAR images of the same target area. From space, there are two ways to achieve this. The first option is to have two SAR antennas orbiting at the same time. This can be achieved by having them on the same platform, as implemented in the Shuttle Radar Topography Mission (SRTM), or by having a satellite constellation as suggested by Massonet (2001). The second option is to acquire the same scene with the same SAR antenna at two different times. This latter solution is mostly used with space-borne SAR systems and it is called repeat-pass interferometry. For this interferometric technique to be applicable, data sets must be obtained when the scene is viewed from almost the same look angle for each of the passes. Figure 2-10 illustrates a simplified interferometric imaging geometry,
The second version of beams, Figure 1 c/d, also uses a common stripmap beam on transmit. On receive an am- plitude taper is applied along elevation over the elements of the phased array. For one beam, the lower half of the array is attenuated by 20 dB and effectively only the up- per half is used to receive. For the other beam, the ta- pering is done in the reverse sense, such that the lower half is used to receive the signals. Two phase centres are generated like this. The signals of the two resulting phase centres can then be evaluated using digital beamforming techniques as they would be applied to a system using a direct radiating array.
SyntheticApertureRadar can be considered nowadays as an established and mature technology to obtain high-resolution two-dimensional reflectivity images of the Earth surface in nearly all weather conditions and independently of the day-night cycle. During a first period, ranging from their conception in the 1950s to the beginning of the 1990s, SAR systems were characterized by a single acquisition channel, and it was proven that this technology allows the observation and characterization of the Earth surface in the microwave region of the electromagnetic spectra. At the beginning of the 1990s, SAR technology started to show its important potential with the availability of multichannel or multidimensional SAR system configurations. Among them, it is worth to mention: interferometric techniques (InSAR), using single- or multiple baselines configurations, polarimetric diversity (PolSAR), or the combination of these approaches called polarimetric SAR interferometry (PolInSAR). It is also important to note that in the recent years, the use of time diversity, in combination with any of the previously introduced multidimensional configurations, has emerged as a new and promising research field to improve the analysis of the areas being imaged, but also the observation and characterization of dynamic processes. The importance and interest on the diﬀerent multidimensional SAR system configurations is partly due to the increase of the number of radar observables but primarily to the fact that the acquired multidimensional signals are sensitive to diﬀerent biophysical and geophysical properties of the Earth surface and may then be used for quantitative retrieval purposes.
The TomoSAR technique usually employs a stack of images acquired in a repeat-pass fashion, which are characterized by slightly different sensor positions. Let us denote the axis normal to the azimuth-range imaging plane as elevation s. We work with the following well-established model which can be found in, e.g.,