Nach oben pdf Multidimensional Waveform Encoding for Synthetic Aperture Radar Remote Sensing

Multidimensional Waveform Encoding for Synthetic Aperture Radar Remote Sensing

Multidimensional Waveform Encoding for Synthetic Aperture Radar Remote Sensing

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 near range 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 [10].
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Multidimensional Radar Waveforms: A New Paradigm for the Design and Operation of Highly Performant Spaceborne Synthetic Aperture Radar Systems

Multidimensional Radar Waveforms: A New Paradigm for the Design and Operation of Highly Performant Spaceborne Synthetic Aperture Radar Systems

III. 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 sequence 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 near range as illustrated in Fig. 4. As a result, the radar echoes from dif- ferent 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 over- lap of the radar echoes from the different sub-swaths is then resolved in the spatial domain by digital beamforming on receive. This a posteriori processing can be performed off-line on the ground, which has the further advantage that no information about the spatial structure of the re- corded radar data will be lost, thereby enabling e.g. a sup- pression of directional interferences or jamming signals and avoiding the mountain clipping problem of the real- time SCORE technique as discussed in [10].
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Forest vertical structure characterization for Biomass estimation with Remote Sensing systems (synthetic aperture radar): Spatial scale implications

Forest vertical structure characterization for Biomass estimation with Remote Sensing systems (synthetic aperture radar): Spatial scale implications

A methodology capable to invert biomass at different scales is of especial importance to  estimate AGB globally and will facilitate the estimation from different remote sensors and in  d[r]

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Assessment of Image Quality of Waveform-Encoded Synthetic Aperture Radar Using Real Data

Assessment of Image Quality of Waveform-Encoded Synthetic Aperture Radar Using Real Data

In order to assess the system performance, and thus the quality of the resulting corrupted im- age, due to the range ambiguity, four di↵erent performance figures are evaluated after performing the proposed multi-focus post-processing. In particular, these measures are obtained by exploiting the separate knowledge of the useful and range ambiguous signals within the simulation context, thus making the system performance analysis not possible in practice, as only the sum of the two super- imposed signals is available. The proposed performance measures aim to assess the just presented waveform-encoded SAR system with reference to a conventional SAR one, without waveform varia- tion on transmit. Also, they allow evaluating the employment of a multi-focus post-processing with a thresholding and blanking approach and based on a contrast minimization method, for threshold selection, on the focused superimposed signal matched to the ambiguity, with reference to the mere waveform encoding, as well as the best achievable performance resulting from the minimization of the total error, in the end of the post-processing chain, after focusing matched to the useful signal. The main performance figure is given by the computation of the total error after focusing matched to the desired echo; it allows understanding the image quality improvement, exploiting the presented waveform-encoded SAR concept, compared to a conventional SAR system, as well as the goodness of the proposed multi-focus post-processing with a contrast minimization-based thresholding and blank- ing approach (to be applied in practice), with reference to both the mere waveform encoding and the best achievable performance, for di↵erent system and processing parameters, e.g. processed Doppler bandwidth and block size, and several range ambiguity strengths (Section 5.2). In the specific, the total error is defined with reference to the ambiguity-free image, as
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Predictive Quantization for Staggered Synthetic Aperture Radar Systems

Predictive Quantization for Staggered Synthetic Aperture Radar Systems

A big challenge for future spaceborne remote sensing missions is now turning to the estimation and long-term monitoring of dynamic processes in the Earth’s environmental system, such as deformation events, forest and biomass change, and ocean surface cur- rents. The German Aerospace Center (DLR) is investigating an innovative single-pass interferometric and fully polarimetric L-band radar mission, named Tandem-L, which ex- ploits innovative high-resolution wide swath SAR modes, together with the use of large bandwidths, high pulse repetition frequencies, and multiple acquisition channels, result- ing in an achievable swath width of about 350 km on ground. Such an increase in term of coverage has as a main drawback the generation of a huge amount of onboard data, which is of around 8 Terabytes per day. One of the proposed solutions to reduce the resulting onboard data reduction suggests to perform a complex onboard processing (i.e. an onboard interpolation, low-pass filtering and decimation) and allows a data reduction up to 50% [23][21]. On the other hand, the onboard computational memory required for the data reduction processing is at the limit of the hardware components, leading to high energy consumption. Moreover, the practical realization of the technique is very complex, including many specific coefficients which must be correctly selected during acquisition. The research of alternative solutions is therefore of great interest in order to have differ- ent options to choose for the mission development, which motivates the present master thesis. In this work, a data reduction strategy based on Linear Predictive Coding (LPC) is investigated in the context of Tandem-L. The method has been designed to reduce the complexity as much as possible while achieving a certain data reduction. A mathemati- cal formulation for the novel technique is an interesting goal for understanding in which situation the present method can be more or less efficient. The resulting performance has been verified through Monte Carlo simulations in order to evaluate the solution under different aspects (i.e. final performance versus resulting system complexity). Moreover, other complications introduced by the Tandem-L system such as the presence of missing samples (so-called gaps) during the acquisition, have been investigated and successfully solved through novel coding strategies.
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Relativistic Effects in Bistatic Synthetic Aperture Radar

Relativistic Effects in Bistatic Synthetic Aperture Radar

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 [38]–[44]. 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.
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A Tutorial on Synthetic Aperture Radar

A Tutorial on Synthetic Aperture Radar

Symmetry assumptions about the distribution of ele- mentary scatterers within the resolution cell simplify the scattering problem and reduce the number of independent parameters of [ ] T (or [ ] C ) allowing qualitative and quanti- tative conclusions about the scattering behavior [35], [42], [43]. Besides reciprocity, three special cases of symmetry are important in radar remote sensing applications: Reflection, rotation and azimuthal symmetry. Reflection symmetric media are characterized by a symmetry plane that contains the line-of-sight so that for any scatterer located at one side of the plane a mirrored scatterer at the other side of the plane exists. In this case the correlation between the co- and cross- polarized elements becomes zero. The resulting [ ] T matrix contains only five independent parameters in form of three real diagonal elements and one single non-zero complex off-diagonal element (i.e., the correlation between the co- polarized elements). The majority of natural distributed scatterers is reflection symmetric. In the case of rotation symmetry, the spatial distributions of elementary scatterers do not change when rotated about the line-of-sight (LOS) axis. Accordingly, the scattering behavior of such media is invariant under the line-of-sight rotations and the resulting coherency matrix contains only three independent parame- ters in form of two independent real diagonal elements and one non-zero imaginary off-diagonal element. This is typi- cal for gyrotropic random media, as given for example by a random distribution of helices. When both, reflection and rotation symmetry applies, the medium is said to be azi- muthally symmetric: All planes including the line-of-sight are reflection symmetry planes. Consequently, all three off- diagonal elements of the coherency matrix become zero, and only two diagonal elements are independent, the num- ber of independent parameters reduces to 2. This is the case for volumes consisting of random distributions of ellipsoids.
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Spaceborne Radar Technologies for Earth Remote Sensing

Spaceborne Radar Technologies for Earth Remote Sensing

A novel approach to exploit the large SAR antenna array is the use of multi-dimensional waveform encoding for the transmitted radar pulses. The illustration in Fig. 2 visualizes the difference between a spatio-temporally waveform encoding (right) and a transmit pulse (left) as used in conventional SAR imaging modes and systems. A simple example for a multi-dimensional waveform encoding in space and time is a mere switching between different antenna beams and/or sub-aperture elements during each transmitted pulse. The overall PRF remains unaltered in this case. Full range resolution within each sub-beam is achieved by concatenating multiple chirp signals in a saw-tooth like frequency modulation (or any other sequence of full bandwidth and possibly even mutually orthogonal waveforms). The scheme allows a staggered illumination of a large area during each pulse, thereby supporting a systematic distribution of the available signal energy within this area. The concept of
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Motion Compensation for Near-Range Synthetic Aperture Radar Applications

Motion Compensation for Near-Range Synthetic Aperture Radar Applications

A number of algorithms for focusing the raw SAR data have been developed since its debut in 1950s. Most of them were originally developed for remote sensing app- lications. Therefore some approximations and assumptions due to the restrictions of the hardware, e.g. antenna beamwidth, squint angle, the computation ability of signal processor, have been made when focusing the raw SAR data. The most accurate SAR algorithm is the time-domain correlation algorithm [42], which can be used to pro- cess the SAR data acquired with arbitrary beam-width and space sampling trajectory. Neither restriction on the antenna’s beamwidth nor motion compensation need to be applied. However, it requires a very high computational effort, which makes it im- practical for any true real-time SAR application given the performance of a realistic hardware environment [43, 44]. By restricting the space sampling interval to uniform, frequency-domain algorithms, which perform the focusing in frequency domain with the power of FFT, can substantially reduce the computational burden.
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Smart Multi-Aperture Radar Techniques for Spaceborne Remote Sensing

Smart Multi-Aperture Radar Techniques for Spaceborne Remote Sensing

If spaceborne synthetic aperture radar (SAR) sensors were categorized according to their operational flexibility, four generations could be identified. The first sensors were on/off sensors with a single fixed mode of operation (fixed beam, in- cidence angle, bandwidth, etc.) Later, sensors were developed which could be operated in multiple modes, such as StripMap, ScanSAR or SpotLight; however the macro-based command- ing was basically restricted to selecting a specific mode without the flexibility to alter/access individual instrument set- tings. Current SAR sensors –third generation– offer a greater flexibility in commanding nearly each individual parameter of the instrument (see T ERRA SAR-X [1] for example). This can be understood as providing the basic building blocks to construct any operation mode in combination with any possible instrument setting. However, even current sensors do not offer a satisfactory solution to the “fundamental limitation of SAR sensors” which can be described in short as the incapability to simultaneously provide high resolution and wide coverage [2], instead they offer the flexibility to choose a compromise between high resolution or wide coverage.
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Bathymetry using Synthetic Aperture Radar (SAR) satellites

Bathymetry using Synthetic Aperture Radar (SAR) satellites

Synthetic Aperture Radar (SAR) is an active remote sensing technology. Radar satellites emit and receive their own signals and, hence, do not need to rely on sunlight for their acquisitions. Another benefit compared to optical satellites is SARs ability to look through clouds, which means data acquisition is very reliable.

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Hurricane Monitoring With Spaceborne Synthetic Aperture Radar

Hurricane Monitoring With Spaceborne Synthetic Aperture Radar

The spaceborne active remote sensing have the unique capability of observing sea surface through cloud, which has been playing an important role of monitor- ing response of sea surface under extreme weather situations. Scatterometers on board the European Remote Sensing (ERS), the Quick Scatterometer (QuikSCAT), and the Meteorological Operational (MetOp) satellites are particularly suitable for measurements of sea surface wind field, as both wind direction and wind speed can be derived without needing external information. Another active remote sensing instrument, spaceborne Synthetic Aperture Radar (SAR), e.g., the ERS-1/2 SAR, ENVISAT/ASAR, RADARSAT-1/2, TS-X/TD-X and Cosmo-Skymed, can not only provide sea surface backscatter intensity like scatterometer, but also image the sea sur- face in two-dimension with large spatial coverage and high spatial resolution, which provides abundant oceanic and atmospheric information of TCs, such as hurricane- generated long swell waves in small scales [ 3 , 4 ], hurricane/typhoon eye morphology [ 5 ] and roll vortices occurred in marine boundary layer [ 6 ] in meso-scale.
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Geosynchronous synthetic aperture radar: design and applications

Geosynchronous synthetic aperture radar: design and applications

Typically, EO satellites are placed in Low Earth Orbit (LEO), to take advantage of the shorter slant range. The key limitation of LEO for remote sensing is the difficulty of providing frequent timely images of an area. A single satellite typically images a swath 100-200 km wide steerable within a field of view 500 km across. LEO orbit periods are approximately 100 minutes, and in this time, the Earth has turned almost 3000 km at the equator: it thus takes ~3000/500=6 days to obtain complete access. Envisat actually uses a 35-day repeat orbit, which means a repeat period of 5 weeks for interferometry, and 1-2 weeks (at mid-latitudes) for imaging. Processes with timescales shorter than these periods are difficult to measure usefully. Satellites in geosynchronous orbit, on the other hand, have a permanent view of 1/3 of Earth’s surface so that, as soon as one image is complete, the next can be started. Allowing for the time required to acquire the image this means that a geosynchronous satellite could provide in principle several images each day of any location in the field of view. An inconvenience caused by the geosynchronous orbit is its fixed longitude. This implies that only a constellation of at least three satellites can guarantee a nearly global coverage. Even so, polar zones cannot be imaged with nominal resolution. However, global coverage is not always an essential requisite for a space system. The problem that many satellite service providers have to tackle is that the request for satellite products is concentrated in the most industrialised regions of the globe therefore a system that is able to provide nearly continuous imaging of a certain region may prove to be advantageous for certain customers.
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Digital Beamforming Techniques for Spaceborne Radar Remote Sensing

Digital Beamforming Techniques for Spaceborne Radar Remote Sensing

A simple example for a non-separable waveform en- coding in space and time is a mere switching between dif- ferent antenna beams during each single transmit pulse. The overall PRF remains unaltered in this case. Since many of the previously mentioned applications rely on a rather low PRF, the concatenation of multiple sub-pulses to a long pulse poses no technical problem. Full range resolution within each beam is achieved by concatenating multiple chirp signals in a saw-tooth like temporal modu- lation (or any other sequence of full bandwidth and pos- sibly orthogonal waveforms). The advantage of such a scheme is that it may allow the staggered illumination of a large footprint during one pulse, thereby supporting a systematic distribution of the available signal energy among the footprint (Sect. 3.1). A further advantage is the opportunity for improved ambiguity suppression due to the reduced antenna beamwidth for each sub-pulse (Sect. 3.2). The concept can of course be extended to an arbi- trary spatiotemporal radar illumination where each direc- tion has its own temporal transmit signal with different power, bandwidth, and phase code. The selection of the spatiotemporal excitation coefficients could even be made adaptive by evaluating previously recorded samples. By this, a closed loop will be formed between the sensor and the environment, which allows for a maximization of the information content (or “radar channel” capacity) for a given RF power budget. The following subsections illus- trate some opportunities arising from such a spatiotempo- ral radar signal encoding.
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Digital Beamforming for Radio Frequency Interference Suppression in Synthetic Aperture Radar

Digital Beamforming for Radio Frequency Interference Suppression in Synthetic Aperture Radar

Equivalent Isotropic Radiated Power Earth-Moon-Earth Frequency Allocations in Remote Sensing Fast Fourier Transform Frequency Modulation Inverse Fast Fourier Transform Interference-Noise[r]

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Advanced Synthetic Aperture Radar Based on Digital Beamforming and Waveform Diversity

Advanced Synthetic Aperture Radar Based on Digital Beamforming and Waveform Diversity

Digital beamforming on transmit allows furthermore a flexible distribution of the RF signal energy on the ground. This enables not only a switching between different SAR modes like Spotlight, ScanSAR and HRWS stripmap, but it allows also for the simultaneous combination of multiple imaging modes in one and the same data acquisition. An example for such an interleaved operation is a spotlight imaging of an area of high interest in combination with a simultaneous wide swath SAR mapping for interferometric applications. This can be achieved by enhancing the multidimensional waveform encoding with additional sub-pulses that steer highly directive transmit beams to some specific areas on the ground as illustrated in Figure 11 on the left. By this, one obtains a high Tx gain and a longer illumination time along the synthetic aperture, which will improve both the radiometric and the geometric resolution for the localized areas of high interest. Such a hybrid mode is well suited to satisfy otherwise contradicting user requirements like the conflict between a continuous interferometric background mission and a high-resolution imaging request.
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Multidimensional Waveform Encoding: A New Digital Beamforming Technique for Synthetic Aperture Radar Remote Sensing

Multidimensional Waveform Encoding: A New Digital Beamforming Technique for Synthetic Aperture Radar Remote Sensing

One example for multidimensional waveform encoding is intrapulse beamsteering in elevation. This enables an illumi- nation 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 major difference that each transmitted pulse now illuminates not only one but all subswaths simultaneously. The illumination sequence within the Tx pulse can, in principle, be arranged in any order. An interesting opportunity arises if we start from a far-range illumination and subsequently proceed to the near range, as shown in Fig. 8. As a result, the radar echoes from different subswaths will overlap in the receiver, as shown on the upper right part of Fig. 8. The overall receiving window can, hence, be shortened, thereby reducing the amount of data to be recorded and stored on the satellite. In other words, the spatial data redundancy discussed in Section II is substantially reduced, since the receiver now obtains radar echoes from multiple directions simultaneously. This example illustrates the potential of multidimensional waveform encod- ing for data compression without sophisticated real-time on- board processing. The information space provided by multiple Rx apertures can moreover be filled by using sub-pulses with different Tx polarisations which allows for the implementation of a fully-polarimetric SAR without the necessity to increase the PRF by a factor of two. The temporal overlap of the radar echoes from the different subswaths and/or polarisations is then resolved in the spatial domain by digital beamform-
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Digital beamforming and multidimensional waveform encoding for sapceborne radar remote sensing

Digital beamforming and multidimensional waveform encoding for sapceborne radar remote sensing

The combination of spatio-temporal radar waveform encoding and digital beamforming on receive is an innovative concept which enables new and very powerful SAR imaging modes for a wide range of remote sensing applications. Examples are improved performance and ambiguity suppression, moving object indication, as well as redundancy reduction in large receiver arrays. The opportunity for beamforming on transmit enables furthermore a flexible distribution of the transmitted RF signal energy on the ground, which allows for the combination of different imaging modes like a simultaneous high resolution spot-light like mapping of areas of high interest in combination with a simultaneous wide swath mapping (Fig. 7). Such a hybrid mode is well suited to satisfy otherwise contradicting user requirements like e.g. the conflict between a continuous interferometric background mission and a high resolution data acquisition request within a wide incident angle range. The data acquisition could even be made adaptive where more system resources are devoted to areas of high interest and/or low SNR, thereby maximizing the
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Synthetic aperture radar remote sensing for landfill monitoring

Synthetic aperture radar remote sensing for landfill monitoring

With regard to SAR images, the influence of pixel size for distributed targets has been partially examined by Nesti et al. (1996). They conclude that the same target appears different depending on the spatial resolution of the SAR processor. This has an immediate impact on the estimation of the backscattering coefficient σ 0 . Their results show that with spatial resolutions smaller than 2 AC length of the target, the statistics of the backscattered signal do not follow those of the speckle model presented in Section 2.1.3. For larger pixel sizes, the experimental data become instead consistent with the Rayleigh model. This finding substantially confirms the results presented in Sarabandi and Oh (1995) which, based on numerical simulations, show that a correct estimate for the σ 0 can be obtained with pixel sizes above 2 AC length. Nesti et al. (1996) do not specifically discuss the spatial structure variation. They simply notice that “in the high resolution image, there are many bright spots sparsely distributed over the surface, whereas in the low resolution image, there are fewer and larger spots almost uniformly covering the entire surface”. In other words, an increasing resolution progressively reveals a different structure of the target, theoretically reaching the point at which each individual scatterer becomes observable and dominates the return in the pixel. This does not mean that the wave-target scattering mechanism which is directly linked to the frequency and polarisation of the SAR system actually changes. For instance, the 5 multilook averaging process in azimuth described in Section 5.2 suitably reduces the speckle effect of SAR imagery but it may also hide texture features which are unrelated to the speckle models of homogeneous targets. Furthermore, this analysis can also be linked to the Ulaby et al. (1982) criteria used in Section 3.1.3 such that horizontal spacing distance in the AC length estimation must be ≤ 0 . 1 λ .
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Multidimensional Waveform Encoding for Spaceborne Synthetic Aperture Radar

Multidimensional Waveform Encoding for Spaceborne Synthetic Aperture Radar

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.
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