Bistatic Synthetic Aperture Radar Research Articles

Trajectory Planning of Cellular-Connected UAV for Communication-Assisted Radar Sensing

Being a key technology for beyond fifth-generation wireless systems, joint communication and radar sensing (JCAS) utilizes the reflections of communication signals to detect foreign objects and deliver situational awareness. A cellular-connected unmanned aerial vehicle (UAV) is uniquely suited to form a mobile bistatic synthetic aperture radar (SAR) with its serving base station (BS) to sense over large areas with superb sensing resolutions at no additional requirement of spectrum. This paper designs this novel BS-UAV bistatic SAR platform, and optimizes the flight path of the UAV to minimize its propulsion energy and guarantee the required sensing resolutions on a series of interesting landmarks. A new trajectory planning algorithm is developed to convexify the propulsion energy and resolution requirements by using successive convex approximation and block coordinate descent. Effective trajectories are obtained with a polynomial complexity. Extensive simulations reveal that the proposed trajectory planning algorithm outperforms significantly its alternative that minimizes the flight distance of cellular-aided sensing missions in terms of energy efficiency and effective consumption fluctuation. The energy saving offered by the proposed algorithm can be as significant as 55%.

Research on Configuration Constraints of Airborne Bistatic SARs.

Based on the analysis of the airborne bistatic synthetic aperture radar (SAR) imaging geometric mode, an extended nonlinear chirp scaling algorithm is employed to simulate and verify the imaging effect of the bistatic SARs. A gradient theory-based two-dimensional resolution bistatic SAR model is proposed, and the constraints of the multi-platform flight trajectory parameters meeting the imaging accuracy of the bistatic SAR are analyzed. Finally, through the bistatic SAR imaging simulation of cooperative flight trajectories under various situations, the spatial configuration constraint envelope between the flight vehicles to achieve the optimal resolution is revealed. The results of this paper will provide a theoretical reference for the SAR application in formation flight control.

Deep Learning for Mapping Tropical Forests with TanDEM-X Bistatic InSAR Data

The TanDEM-X synthetic aperture radar (SAR) system allows for the recording of bistatic interferometric SAR (InSAR) acquisitions, which provide additional information to the common amplitude images acquired by monostatic SAR systems. More concretely, the volume decorrelation factor, which can be derived from the bistatic interferometric coherence, is a reliable indicator of the presence of vegetation and it was used as main input feature for the generation of the global TanDEM-X forest/non-forest map, by means of a clustering algorithm. In this work, we investigate the capabilities of deep Convolutional Neural Networks (CNNs) for mapping tropical forests at large-scale using TanDEM-X InSAR data. For this purpose, we rely on a U-Net architecture, which takes as input a set of feature maps selected on the basis of previous preparatory works. Moreover, we design an ad hoc training strategy, aimed at developing a robust model for global mapping purposes, which has to properly manage the large variety of different acquisition geometries characterizing the TanDEM-X global data set. In addition to detecting forest/non-forest areas, the CNN has also been trained to detect water surfaces, which are typically characterized by low values of coherence. By applying the proposed method on single TanDEM-X images, we achieved a significant performance improvement with respect to the baseline clustering approach, with an average F-score increase of 0.13. We then applied such a model for mapping the entire Amazon rainforest, as well as the other tropical forests in Central Africa and South-East Asia, in order to test its robustness and generalization capabilities, and we observed that forests are typically well detected as contour closed regions and that water classification is reliable, too. Finally, the generated maps show a great potential for mapping temporal changes occurring over forested areas and can be used for generating large-scale maps of deforestation.

A Novel Frequency-Domain Focusing Method for Geosynchronous Low-Earth-Orbit Bistatic SAR in Sliding-Spotlight Mode

The low-earth-orbit synthetic aperture radar (SAR) can achieve enhanced remote-sensing capabilities by exploiting the large-scale and long-duration beam coverage of a geosynchronous (GEO) SAR illuminator. Different bistatic imaging modes can be implemented by the steering of an antenna beam onboard the LEO receiver, such as high-resolution sliding-spotlight mode. In this paper, the accurate focusing of GEO-LEO bistatic SAR (GEO-LEO BiSAR) in sliding-spotlight mode is investigated. First, the two major problems of the accurate bistatic range model, i.e., curved trajectory within long integration time and ‘stop-and-go’ assumption error, for sliding-spotlight GEO-LEO BiSAR are analyzed. Then, a novel bistatic range model based on equivalent circular orbit trajectory is proposed to accurately represent the range history of GEO-LEO BiSAR in sliding-spotlight mode. Based on the proposed range model, a frequency-domain imaging method is put forward. First, a modified two-step preprocessing method is implemented to remove the Doppler aliasing caused by azimuth variance of Doppler centroid and beam steering. Then, an azimuth trajectory scaling is formulated to remove the azimuth variance of motion parameters due to curved trajectory. A modified frequency-domain imaging method is derived to eliminate the 2-D spatial variance and achieve accurate focusing of the echo data. Finally, imaging results and analysis on both simulated data and real data from an equivalent BiSAR experiment validate the effectiveness of the proposed method.

A chirp scaling algorithm based on the method of series reversion for bistatic SAR

For the problem that the existing chirp scaling algorithm based on the method of series reversion (MSRCSA) needs to approximate the range cell migration (RCM) into a linear term of range and causes a larger range cell migration correction (RCMC) error, we propose an improved MSRCSA, which is able to focus azimuth-variation bistatic SAR. Firstly, to avoid the linear approximation for RCM, this paper derives a new curvature factor varying with the azimuth frequency and range, utilizing to describe RCM without any approximation. Secondly, since the new curvature factor is different with the traditional curvature factor varying only with the azimuth frequency, a new scaling function is given to modulate the echo signal so that the required range-variant RCMC shift can be implemented by using phase multiplies. Thirdly, the proposed MSRCSA is implemented by utilizing three phase multiplications and four fast Fourier transforms (FFTs). Finally, the good performance of the proposed MSRCSA is examined by comparing the imaging result of the proposed MSRCSA with that of the existing MSRCSA.

An Adjusted Frequency-Domain Algorithm for Arc Array Bistatic SAR Data with One-Moving Transmitter.

Arc array synthetic aperture radar (AA-SAR), which can observe the scene in all directions, breaks through the single view of traditional SAR. However, the concealment of AA-SAR is poor. To mitigate this, arc array bistatic SAR (AA-BiSAR) with the moving transmitter is proposed, it has the advantages of good concealment and can expand the imaging scene, and improve the flexibility of the system. The imaging geometry including the signal model is established, and a range frequency-domain algorithm based on keystone transform (KT) is proposed in this paper. In the first step, the slant range equation is approximated by Taylor series expansion to compensate for the residual phase caused by the transmitter motion. In the second step, the range cell migration between the range and azimuth is eliminated through the KT method in the range frequency-domain. In the third step, using the data after range cell migration correction in step 2, an azimuth pulse compression is performed to obtain the focused image. In addition, the spatial resolution of the AA-BiSAR system is analyzed in detail. Finally, three simulation results verify the effectiveness of the proposed algorithm and the change in the spatial resolution.

An Efficient Backprojection Algorithm Based on Wavenumber-Domain Spectral Splicing for Monostatic and Bistatic SAR Configurations

This paper introduces a fast backprojection synthetic aperture radar (SAR) imaging algorithm based on wavenumber-domain spectral splicing. The traditional fast backprojection (FBP) algorithm establishes the polar coordinate system with the center of the sub-aperture as the origin. Therefore, the coordinates of the image obtained from each sub-aperture are different. Sub-aperture images must be projected to a uniform coordinate system before they can be coherently superimposed to form the final image, which requires a large amount of calculation. In order to deal with this problem, this paper proposes a novel imaging method, which uses the same polar coordinate system for each sub-aperture. The sub-aperture images are then spliced in the wavenumber-domain, and directly added after upsampling. This method avoids the projection from each sub-aperture to the uniform coordinate system, thus improving the imaging accuracy and efficiency. At the same time, the algorithm is suitable for various configurations, and can achieve good imaging results for bistatic forward-looking SAR and high-speed mobile platform. Finally, simulations are presented to demonstrate the effectiveness of the algorithm.

Soil Moisture Retrieval Using Multistatic L-Band SAR and Effective Roughness Modeling

The interest in bistatic SAR systems for soil moisture monitoring has grown over recent years, since theoretical studies suggest that the impact of surface roughness on the retrieval of soil moisture decreases when multistatic, i.e., simultaneous mono- and bistatic, radar measurements are used. This paper presents a semi-empirical method to retrieve soil moisture over bare agricultural fields, based on effective roughness modeling, and applies it to a series of L-band fully-polarized SAR backscatter and bistatic scattering observations. The main advantage of using effective roughness parameters is that surface roughness no longer needs to be measured in the field, what is known to be the main source of error in soil moisture retrieval applications. By means of cross-validation, it is shown that the proposed method results in accurate soil moisture retrieval with an RMSE well below 0.05 m3/m3, with the best performance observed for the cross-polarized backscatter signal. In addition, different experimental SAR monostatic and bistatic configurations are evaluated in this study using the proposed retrieval technique. Results illustrate that the soil moisture retrieval performance increases by using backscatter data in multiple polarizations simultaneously, compared to the case where backscatter observations in only one polarization mode are used. Furthermore, the retrieval performance of a multistatic system has been evaluated and compared to that of a traditional monostatic system. The recent BELSAR campaign (in 2018) provides time-series of experimental airborne SAR measurements in two bistatic geometries, i.e., the across-track (XTI) and along-track (ATI) flight configuration. For both configurations, bistatic observations are available in the backward region. The results show that the simultaneous use of backscatter and bistatic scattering data does not result in a profound increase in retrieval performance for the bistatic configuration flown during BELSAR 2018. As theoretical studies demonstrate a strong improvement in retrieval performance when using backscatter and bistatic scattering coefficients in the forward region simultaneously, the introduction of additional bistatic airborne campaigns with more promising multistatic SAR configurations is highly recommended.

수치표고모델을 이용한 지형 레이다 반사 면적 생성 및 바이스태틱 SAR 영상합성 기술 연구

In this study, we simulated target data for bistatic synthetic aperture radar (SAR) using a digital elevation model (DEM), raw data acquisition, and image synthesis. Using the DEM, target data similar to the actual topography could be generated. In particular, the reflectivity of the target was calculated from the geometrical structure between the SAR and the target. To verify and estimate the proposed method, we generated target data using bistatic SAR system modeling and simulated raw data acquisition. Finally, we compared the DEM and SAR images synthesized using the polar format algorithm (PFA).

A Long-Time Coherent Integration STAP for GEO Spaceborne-Airborne Bistatic SAR

A geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO SA-BSAR) system is an important technique to achieve long-time moving target monitoring over a wide area. However, due to special bistatic configuration of GEO SA-BSAR, two major challenges, i.e., severe range migration and space-variant Doppler parameters for moving targets, hinder the moving target indication (MTI) processing. Traditional SAR MTI methods, which do not take the challenges into consideration, will defocus the moving targets, leading to a loss of the signal-to-noise ratio (SNR). To focus moving targets and estimate motion parameters accurately, long-time coherent integration space-time adaptive processing (LTCI-STAP) is proposed for GEO SA-BSAR MTI in this paper. First, a modified adaptive spatial filtering based on the bistatic signal model is performed to suppress the clutter. Then, an LTCI filter bank is constructed to achieve range migration correction and moving target focusing, which yields the optimal output signal and filtering parameters. Finally, constant false alarm rate (CFAR) detection is carried out to determine the targets, and the space-variant Doppler parameters, solved from the filtering parameters, are used for estimating moving target positions and velocities. Simulations verify the effectiveness of our method.

SDR-Implemented Passive Bistatic SAR System Using Sentinel-1 Signal and Its Experiment Results

A fixed-receiver mobile-transmitter passive bistatic synthetic aperture radar (MF-PB-SAR) system, which uses the Sentinel-1 SAR satellite as its non-cooperative emitting source, has been developed by using embedded software-defined radio (SDR) hardware for high-resolution imaging of the targets in a local area in this study. Firstly, Sentinel-1 and the designed system are introduced. Then, signal model, signal pre-processing methods, and effective target imaging methods are presented. At last, various experiment results of target imaging obtained at different locations are shown to validate the developed system and the proposed methods. It was found that targets in a range of several kilometers can be well imaged.

Assessing the Potential of Fully Polarimetric Mono- and Bistatic SAR Acquisitions in L-Band for Crop and Soil Monitoring

Theoretical studies have shown that the use of simultaneous mono- and bistatic synthetic aperture radar (SAR) data could be beneficial to agriculture and soil moisture monitoring. This study makes use of extensive ground-truth measurements and synchronous high-resolution fully polarimetric mono- and bistatic airborne SAR data in L-band to assess and compare the sensitivity of mono- and multistatic systems to the maize canopy row structure and biophysical variables, as well as to soil moisture and surface roughness in both vegetated and bare fields. The effect of the row structure of maize crops is assessed through the impact of the orientation of the planting rows relative to the sensor beam on microwave scattering measurements. The results of this analysis suggest that the row orientation of maize crops has a significant influence on both mono- and bistatic scattering measurements in both copolarizations, and especially, in HH, while the cross polarizations are not affected. Furthermore, the study also shows through a linear regression analysis that bistatic data, even with a very small bistatic baseline, can provide valuable additional information for maize crop biophysical variable retrieval, which however does not appear to be the case for soil moisture retrieval over bare soils.

Focusing High Maneuvering Bistatic Forward-Looking SAR With Stationary Transmitter Using Extended Keystone Transform and Modified Frequency Nonlinear Chirp Scaling

This article presents an imaging solution for high maneuvering bistatic forward-looking SAR with stationary transmitter (STHM-BFSAR). In STHM-BFSAR configuration that the transmitter is mounted on a stationary platform in side-looking mode while the receiver does high-speed maneuvering in forward-looking mode, high speed and acceleration induced by high dynamic characteristics of receiver in both along-track and height direction cause larger range cell migration (RCM) and more severe 2-D spatial variation of Doppler characteristics, which makes it more difficult to obtain well-focused bistatic SAR image. Furthermore, different from the general airborne bistatic SAR, STHM-BFSAR has a larger azimuth cubic phase term that exceeds <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pi /4$</tex-math></inline-formula> rad, which seriously affects the image quality. To deal with these problems, an imaging algorithm based on extended keystone transform (EKT) and modified frequency nonlinear chirp scaling (FNCS) is proposed in this article. EKT can correct spatial variant range curvature and bulk linear RCM, and the residual RCM is smaller than traditional keystone transform. The proposed modified FNCS is used to equalize the azimuth-range-dependent Doppler parameters in the frequency domain, which produces lower side-lobes and higher accuracy by compensating for the second-order spatial variation of the azimuth cubic coefficients. The final simulation results in this article verify the effectiveness of the proposed algorithm.

An Optimal Polar Format Refocusing Method for Bistatic SAR Moving Target Imaging

Bistatic synthetic aperture radar (BiSAR) has received more and more attentions because of its forward-looking imaging capability and configuration flexibility. For BiSAR moving target imaging, its non-cooperative motion leads to unknown range cell migration (RCM) and additional phase modulation. Consequently, moving target imaging in BiSAR face two main challenges: 1) The unknown RCM correction and Doppler parameter estimation are tightly coupled. 2) The Doppler parameters of the extended moving target’s different scattering points are different, i.e., the Doppler parameters are spatially variant. To cope with these problems, an optimal polar format refocusing method for bistatic SAR moving target imaging is proposed. First, the main part of tight coupling and spatial variation effects caused by the BiSAR platforms are eliminated, while the moving target is two-dimensional (2-D) defocused and shifted. Then, we analyze the characteristics of two-dimensional defocused and shifted of the moving target in BiSAR, and give the analytical expressions. On this basis, a new bistatic polar format transformation is introduced, in which the degree of freedom of defocusing result is reduced from 2-D to only one-dimension. After that, the parameter estimation and refocusing issues are transformed into a constrained optimization problem (COP), and differential evolution (DE) is applied to solve the COP and obtain the refocusing results. Finally, considering the spatial variation of the extended moving target, the compensation processing is performed to relocate each scattering point. Numerical simulations verify the effectiveness of the proposed method.

Bistatic SAR Clutter-Ridge Matched STAP Method for Nonstationary Clutter Suppression

Clutter suppression is a challenging task in synthetic aperture radar-ground moving target indication (SAR-GMTI). In general, sufficient secondary samples are not easily acquired due to the nonstationary and nonhomogeneous characteristics of bistatic SAR (BiSAR) clutter, resulting in worse clutter suppression results. Recently, space–time adaptive processing based on sparse recovery (SR-STAP) has been developed since its better clutter suppression performance with less samples. However, since the off-grid problem in space–time domain caused by BiSAR’s separate configuration, existing SR-STAP would suffer from severe performance degradation. To address this problem, a clutter-ridge matched STAP (CRM-STAP) method for BiSAR nonstationary clutter suppression is proposed. First, clutter distribution modeling with arbitrary BiSAR configuration is applied to accurately obtain the clutter ridge in space–time domain. Then, keystone transform and time-division processing are applied to correct range cell migration and eliminate Doppler frequency migration, respectively. Next, to solve the off-grid problem, the CRM dictionary is reconstructed via adaptive gradient method, which is established along the direction of clutter ridge and its orthogonal direction. Then, with the constructed CRM dictionary, the clutter covariance matrix (CCM) estimation process is transformed to a multimeasured vector optimization problem, and it can be directly solved by the sparse Bayesian learning algorithm. Finally, based on the estimated CCM, the CRM-STAP filter is built to suppress the nonstationary clutter effectively. Compared with the existing STAP and SR-STAP methods, this method can avoid the performance degradation in clutter suppression caused by the off-grid problem and overcomes the strong nonstationary problem of BiSAR clutter in heterogeneous environments. In October 2020, we have successfully carried out the world’s first airborne BiSAR-GMTI experiment, and the experimental results are given to verify the effectiveness of this method.

An Autofocus Scheme of Bistatic SAR Considering Cross-Cell Residual Range Migration

Benefiting from the capability of forward-looking imaging, ability of receiver radio silence and resistance of jamming, bistatic SAR has extensive potential applications. However, due to its independent dual platform movement, motion error of bistatic SAR is usually more complicated than monostatic SAR and could easily exceed range resolution cell, which will result in both azimuth and range defocusing if not properly compensated. In this paper, an autofocus scheme for bistatic SAR considering cross-cell residual range migration is proposed. In this scheme, cross-cell residual range migration is firstly compensated by estimating bistatic range error through time-frequency analysis. Meanwhile, azimuth phase error is coarsely compensated by using bistatic range error estimation result. Secondly, maximum image sharpness autofocus method is cascaded to further compensate for azimuth residual phase error and enhance the quality of bistatic SAR image. The effectiveness of proposed scheme is verified by simulation and experiment results.

Bistatic SAR Point Spread Function Analysis for Close Proximity Geometries

The goal of this project is to describe the spatially variant imaging performance of bistatic SAR imaging using a localized spatially invariant approximation so as to use spatial Fourier analysis. Bistatic synthetic aperture radar (SAR) has been proposed for use in search and rescue operations, for UAV guidance, and as an all-weather navigational aid for landing aircraft. These applications involve aggressive maneuvering platforms operating at short standoff ranges. Matched filter image formation (MFIF) and the equivalent back-projection image formation algorithm fully capture the intrinsic imaging performance of these geometries. The imaging performance for these imaging configurations is described by the spatially variant point spread function (PSF). The SAR imaging process is described as a non-convolutional integral operation between the scene’s reflectivity function and the PSF. It is desirable to describe the process as a multiplication operation in the spatial Fourier domain but this requires the imaging process to be spatially invariant. The model derived here deals with the spatial variations by segmenting a large scene into smaller regions. Within each region, the spatially variant PSF is locally approximated as a spatially invariant impulse response (IPR). The SAR imaging process is described as a localized convolution operation. This approximation is extremely accurate at the center of a segmented region but degrades for positions displaced from the center. Error analysis is used to determine the maximum size of a segment to limit the error to a given tolerance.

Bistatic SAR Moving Targets Refocus Based on Polar Formatted Phase Error Analysis and Clutter Separation

The refocus process of moving targets in bistatic synthetic aperture radar (SAR) imagery obtained by polar format algorithm (PFA) is discussed in this paper. The analytical expression of the motion phase error in the two-dimensional frequency domain is deduced based on the method of series reversion. The derived phase error equation is utilized to formulate a dictionary, and a sparse-based iteration processing combined with a subtraction operation is proposed to implement the separation of clutter and refocusing of multiple moving targets, respectively. Finally, numerical simulation is performed to verify the validity of the proposed method.

Tilt Phenomenon in Bistatic SAR Image

The tilt phenomenon or skew in bistatic synthetic aperture radar (SAR) images has been known. This can be observed from the SAR image of a point-like scatterer, even with a symmetrical bistatic aperture. This letter explains this phenomenon in bistatic SAR images and shows that the phenomenon depends on the initial and the last positions of the transmitter and receiver platforms. A formula to calculate the tilt angle based on a general bistatic geometry is provided. This formula supports bistatic SAR system design and bistatic image quality measurements.

A Compressive-Sensing Approach for Opportunistic Bistatic SAR Imaging Enhancement by Harnessing Sparse Multiaperture Data

This article introduces a compressive sensing (CS)-based approach for increasing bistatic synthetic aperture radar (SAR) imaging quality in the context of a multiaperture acquisition. The analyzed data were recorded over an opportunistic bistatic setup including a stationary ground-based-receiver opportunistic C-band bistatic SAR differential interferometry (COBIS) and Sentinel-1 C-band transmitter. Since the terrain observation by progressive scans (TOPS) mode is operated, the receiver can record synchronization pulses and echoed signals from the scene during many apertures. Hence, it is possible to improve the azimuth resolution by exploiting the multiaperture data. The recorded data are not contiguous and a naive integration of the chopped azimuth phase history would generate undesired grating lobes. The proposed processing scheme exploits the natural sparsity characterizing the illuminated scene. For azimuth profiles recovery greedy, convex, and nonconvex CS solvers are analyzed. The sparsifying basis/dictionary is constructed using the synthetically generated azimuth chirp derived considering Sentinel-1 orbital parameters and COBIS position. The chirped-based CS performance is further put in contrast with a Fourier-based CS method and an autoregressive model for signal reconstruction in terms of scene extent limitations and phase restoration efficiency. Furthermore, the analysis of different receiver-looking scenarios conducted to the insertion in the processing chain of a direct and an inverse Keystone transform for range cell migration (RCM) correction to cope with squinted geometries. We provide an extensive set of simulated and real-world results that prove the proposed workflow is efficient both in improving the azimuth resolution and in mitigating the sidelobes.