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Recently, a variety of super-resolution (SR) methods have been devoted to enhancing the angular resolution of real beam mapping (RBM) imagery in modern microwave remote sensing applications. When addressing large-scale datasets, however, they suffer from notably high computational complexity due to high-dimensional matrix inversion, multiplication, or singular value decomposition (SVD). To overcome this limitation, this article presents a low-complexity SR strategy based on adaptive low-rank approximation (LRA). Our underlying idea is first to construct a random matrix sketching to sample the raw echo measurements and restore the surface map of reflectivity in a low-dimensional linear space. The resulting low-complexity strategy enables substantial computational complexity reduction for a group of SR methods, at the cost of introducing a manually adjusted LRA parameter. Using the Fourier transform-based antenna analysis method, we further reveal that the LRA parameter that ensures support resolution improvement can be determined by a closed-form function of the aperture length, the wavelength, and the field of view, allowing for adaptively and efficiently selecting the optimal LRA parameter that well balances the tradeoff between LRA error and computational efficiency. We use both simulated and real datasets to demonstrate that the proposed LRA-based SR strategy can provide significant speedup without performance loss. [ABSTRACT FROM AUTHOR]

To realize high azimuth resolution for sea-surface targets, this article proposes a superresolution imaging method that relies on the Weibull distribution. The proposed method introduces the generalized Gaussian distribution and Weibull distribution to represent the statistical distribution function of the target prior information and sea clutter, respectively. The corresponding objective function was derived under the maximum a posteriori (MAP) criterion. To address the nonlinearity of the objective function, this article adopts the Newton–Raphson iterative method to resolve it. Simulations and experimental data assessment indicate that the proposed method has superior superresolution imaging performance compared with other traditional superresolution methods for sea-surface target imaging. [ABSTRACT FROM AUTHOR]

Microwave photonic synthetic aperture radar (MWP-SAR) offers a larger signal bandwidth than conventional SAR, and its theoretical resolution can be improved to centimeter level. To achieve same order of magnitude resolution in the azimuth direction, long synthetic aperture is always required, which results in extremely high requirements for the accuracy of imaging procedure. Compared with the conventional SAR imaging algorithms, two major problems should be considered: 1) the scattering characteristics of target might vary from the frequency of signal and the angle of incidence, which seriously affects the coherence of received echo and 2) the imaging of the MWP-SAR system is more sensitive to motion errors and therefore requires higher accuracy of motion compensation processing. To solve the above issues, a high-precision imaging method for MWP-SAR is proposed. First, based on the attribute scattering center (ASC) model, this article analyzes the target scattering characteristics with different frequencies and incident angles. Then, according to the influence of scattering phase on MWP-SAR imaging, an optimal subaperture division algorithm is proposed to guarantee the coherence of each subaperture data and better imaging results. Furthermore, to compensate for the effects of high-order motion errors, this article proposes a motion error estimation algorithm based on subimage registration, where the full-aperture high-order motion errors can be divided into multiple linear components, and the flight trajectory of platform can be accurately reconstructed. Finally, a full-aperture time-domain high-precision imaging method is presented, and the effectiveness of the proposed formation is verified by both simulation and actual airborne MWP-SAR data processing. [ABSTRACT FROM AUTHOR]

Superresolution methods can be applied to real aperture radar (RAR) to improve its angular resolution by solving an inverse problem. However, traditional superresolution methods are achieved after batch data collection, which requires extensive operational complexity and storage space. To solve this problem for RAR, an online detect-before-reconstruct (DBR) framework is proposed in this article based on the sparse property of targets. First, along the range direction, each sample of the echo data is detected to reduce the computational complexity by reducing the dimension of the effective data. Second, along the azimuth direction, a data-adaptive online processing structure is proposed to reduce the storage requirement for the angular superresolution problem. Finally, within the online processing structure, a target data-adaptive updating strategy is proposed to reduce the number of iterations for each target grid. The online DBR-based framework can effectively reduce the operational complexity caused by the noise values of the echo data. Based on the proposed online processing structure, the storage requirement and the operational complexity of the angular superresolution for an RAR system can be greatly reduced without significant reconstruction performance loss. The results of simulations and experimental data verify the proposed framework. [ABSTRACT FROM AUTHOR]

Cross-resolution enhancement for airborne high-squint radar (AHSR) imagery is mathematically equivalent to the ill-conditioned problem of inverse-scattering reconstruction. Although a variety of inversion methods with regularization can be introduced to advance the field of AHSR imagery, they turn out to be computationally intensive when extended to 2-D (range and cross-range dimension) image formulation due to the range-by-range calculation for the space-variant inversion operators over the full range swath. To tackle the problem of efficiency, this article presents a low-complexity inverse-scattering strategy. Our underlying idea is to equalize the space-variant Doppler centroid embedded in an inversion operator for a reference range cell using Doppler centroid compensation. With the proposed strategy, the necessary computational complexity required for 2-D AHSR inverse-scattering reconstruction can be significantly reduced by requiring only the calculation of the inversion operator, independently of the number of range cells. Our experimental assessment, conducted using both the simulation and real data, demonstrates that our proposed inverse-scattering strategy offers preferable computational reduction in the task of inverse-scattering reconstruction for 2-D AHSR imagery without resolution loss. [ABSTRACT FROM AUTHOR]

Marine radar plays a significant role in ship navigation. However, when contending with interference among cosailing navigation radars, the echo data may be unintentionally corrupted, and it becomes challenging to obtain high-quality imagery using current radar imaging methods. To overcome this problem, an efficient anti-interference imaging framework is presented in this article based on the theory of nonuniform sampling. First, a beam-recursive anti-interference method based on the signal-to-interference-plus-noise ratio (SINR) estimation is proposed to compensate for the shortcoming of the traditional interference rejection method. Second, a nonuniform sampling model is established to well model the echo data with missing samples, which facilitates reconstructing the marine radar imagery from the missing echo data. Finally, a fast super-resolution method based on the dimension-reduction iterative adaptive approach (DRIAA) is proposed to reconstruct the distribution of sea-surface targets at a much lower computational complexity. Simulated and experimental results demonstrate that our anti-interference imaging framework can provide radar imagery with higher quality and lower computational complexity than the existing radar imaging methods in the presence of unintentional interference. [ABSTRACT FROM AUTHOR]

Most existing super-resolution imaging methods fail to work in low signal-to-noise ratio (SNR) condition due to the ill-posed antenna measurement matrix, but the sparse-truncated singular value decomposition (TSVD) method can effectively suppress noise and improve azimuth resolution in low SNR condition. However, the current sparse-TSVD method encounters large computation cost, resulting in a slow algorithm speed. In this work, a fast sparse-TSVD super-resolution imaging method of real aperture radar is proposed. First, the proposed method is based on the results of TSVD, using the truncated unitary matrix and diagonal matrix to reconstruct the signal convolution model. The dimension of the reconstructed antenna measurement matrix reduces from $N \times N$ to $k \times N$ , and the dimension of the reconstructed echo matrix reduces from $N \times 1$ to $k \times 1$ , where $N$ is azimuth sampling points and $k$ is truncation parameter, $N \gg k$. Much of the expensive matrix– multiplication computation can then be performed on the smaller matrices, thereby accelerating the algorithm. Second, an objective function is established as the ${l_{1}}$ constraint based on the regularization strategy. Lastly, this article employs iterative reweighted least square (IRLS) method to solve the objective function, and the dimension of the reversed matrix is lessened from $N \times N$ to $k \times k$ , speeding up the algorithm further. The simulation and real data verify that the proposed algorithm not only improves the azimuth resolution in low SNR condition but also increases computational efficiency compared with the sparse-TSVD method. [ABSTRACT FROM AUTHOR]

Bistatic forward-looking spotlight synthetic aperture radar with stationary transmitter and maneuvering receiver (STMR-BFSSAR) is a promising sensor for various applications, such as the automatic navigation and landing of maneuvering vehicles. Because of the bistatic forward-looking configuration and the receiver’s maneuvers, conventional image formation algorithms suffer from high computational complexity or small size of a well-focused scene if applied to STMR-BFSSAR. In this article, we propose a wavenumber-domain algorithm for STMR-BFSSAR image formation, which is termed the bistatic-range-Doppler-aperture wavenumber algorithm (BDWA). First, a novel range model in bistatic-range and Doppler-aperture coordinate space instead of conventional Cartesian coordinate space is established by employing the elliptic polar coordinate system and the method of series reversion. The novel range model not only makes the echo’s samples to be regular along the direction of the bistatic-range wavenumber axis but also constructs a curved wavefront close to the true wavefront. Second, an operation termed wavenumber-domain gridding is conceived to regularize the echo’s samples along the Doppler-aperture wavenumber axis, which can be implemented by 1-D interpolation. The proposed algorithm significantly outperforms the conventional algorithms in terms of computational complexity and scene size limits. Both point and distributed targets are simulated for two STMR-BFSSAR systems with different parameters. The simulation results verify the validity and superiority of the proposed BDWA. [ABSTRACT FROM AUTHOR]

Forward-looking imaging and target detection are highly desirable in many military and civilian fields, such as search and rescue, sea surface surveillance, airport surveillance, and guidance. However, there is a blind zone of forward-looking imaging for conventional Doppler beam sharpening and synthetic aperture radar. Scanning radar can be utilized to obtain a real beam image of a forward-looking area and implement target detection, while its azimuth resolution is poor due to the limitation of antenna size. Besides, during the processing procedure, imaging and target detection are usually regarded as two independent parts, which means that the imaging result will directly affect the detection performance. In this article, an integrated algorithm of super-resolution imaging and target detection for forward-looking scanning radar is proposed. In this algorithm, first of all, low-rank and sparse constraints as regularization norms are incorporated into the forward-looking scanning radar imaging and the objective function is established. Subsequently, the convex theory is utilized to solve the objective function and transform the problem of simultaneous super-resolution imaging and target detection into an optimization problem. Lastly, the super-resolution imaging and the target detection results are obtained simultaneously by solving the optimization problem using the alternating direction method of multipliers. In addition, simulation and experiment results are given to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Real-aperture radar can be utilized to realize forward-looking imaging by antenna scanning the imaging region. However, low azimuth resolution seriously affects its practical application. Although traditional super-resolution methods could enhance azimuth resolution to a certain extent, effective preservation of contour information for important targets still remains to be a problem. In this article, a method of total variation-sparse (TV-sparse) multiconstraint deconvolution is proposed to improve azimuth resolution of forward-looking imaging as well as preserve contour information of important targets. Since our interested targets usually appear to be sparse, the sparse constraint of the target is introduced first to achieve high resolution of forward-looking images, which may cause the loss of target contour information in the meantime. Second, total variation (TV) constraint is introduced based on the sparse constraint, converting traditional single-constraint super-resolution problem to a multiconstraint problem. We then use the split Bregman algorithm (SBA) to solve the multiconstraint problem, whose solution is the super-resolution image of radar forward-looking region. Compared with traditional super-resolution methods, the proposed method can improve the azimuth resolution of radar forward-looking imaging as well as better restore target contour information by adjusting respective weights of sparse constraint and TV constraint. Finally, the performance of the proposed method is validated with the simulation and measured data. [ABSTRACT FROM AUTHOR]

Because of the poor performance of the conventional total variation (TV) super-resolution imaging method in low signal-to-noise ratio (SNR) condition, a TV super-resolution imaging method based on the truncated singular value decomposition (TSVD) strategy is proposed. First, based on the regularization theory, the TV function is selected as the constraint term to construct objective function. Second, to solve the problem of noise amplification faced by the conventional TV method, this article reconstructs the objective function based on the TSVD strategy, which improves the antinoise performance by discarding small singular values of antenna convolution matrix. Finally, due to the nondifferentiable property of reconstructed objective function, this article utilizes the iterative reweighted norm (IRN) method. Since the influence of the noise is weakened by the TSVD strategy, the proposed method can achieve super-resolution imaging and contour preservation in low SNR condition. The simulation and experimental results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Being capable of providing weather-independent, day-and-night, forward-looking, and high-resolution images, bistatic forward-looking synthetic aperture radar (BFSAR) is a promising sensing technique in applications such as the scene-matching-aided navigation for recently emerging high-speed maneuvering platforms (HMPs). Because of the high speed and the great maneuverability of HMPs and the bistatic forward-looking configuration, conventional image formation algorithms, such as polar format algorithm (PFA), are no longer suitable for HMP-borne BFSAR (HMP-BFSAR). Hence, in this paper, we propose a novel PFA for HMP-BFSAR image formation. In the proposed PFA, a range model, termed as quasi-continuous -move range model, is established by taking the maneuvers of the receiver during pulse propagation into account instead of adopting stop-and-go approximation. Moreover, to take advantage of the collected $k$ -set efficiently, an affine mapping, termed as $k$ -set affine mapping, is conceived to transform the parallelogram-shaped $k$ -set support region to a horizontal and quasi-rectangular one. Furthermore, to compensate for the defocus effect induced by wavefront curvature, a closed-form refocus filter based on the implicit function theorem is derived. Both point target simulation and distributed target simulation are presented in this paper. The simulation results show that the proposed PFA significantly outperforms the conventional PFA in terms of focusing quality and computational efficiency when applied to HMP-BFSAR image formation. [ABSTRACT FROM AUTHOR]

Due to its flexibility and low cost, the bistatic forward-looking synthetic aperture radar (BFSAR) which employs side-looking transmitter and forward-looking receiver has been studied in recent years. Sparsity-based techniques have been applied in the field of BFSAR imaging and show great potential. In sparsity-based BFSAR imaging, compensation of the motion errors is crucial to get a well-focused image. For fields that admit a sparse representation, we propose a sparsity-based imaging approach integrated with motion error estimation and compensation in this paper. First, a novel joint phase-amplitude compensation-based motion error correction scheme is developed to cope with the spatial variance of motion error. Then, an inversion observation model of the range-Doppler algorithm combined with motion error correction is derived, based on which a joint problem of BFSAR imaging and motion error estimation is formulated as a sparse recovery problem and solved in an iterative way, where in each iteration, both image formation and motion error correction are carried out. Experiments on both the simulated and real BFSAR data show that the proposed method can obtain a more accurate estimation result, and generate better focused images compared with the existing methods. [ABSTRACT FROM AUTHOR]

Multiview synthetic aperture radar (SAR) images could provide much richer information for automatic target recognition (ATR) than from a single-view image. It is desirable to find optimal SAR platform flight paths and acquire a sequence of SAR images from appropriate views, so that multiview SAR ATR can be carried out accurately and efficiently. In this paper, a novel optimization framework for multiview SAR ATR is proposed and implemented. The geometry of the multiview SAR ATR is modeled according to the recognition mission and flight environment. Then, the multiview SAR ATR is abstracted and transformed into a constrained multiobjective optimization problem with objective functions considering the tradeoffs between recognition performance and efficiency and security. A specific approach based on convolutional neural network ensemble and constrained nondominated sorting genetic algorithm II is employed to solve the multiobjective optimization, and optimal flight paths and corresponding imaging viewpoints are obtained. The SAR sensor can thus choose an applicable flight path to acquire the multiview SAR images from different tradeoff solutions according to application requirements. Finally, accurate recognition results can be obtained based on those multiview SAR images. Extensive experiments have shown the validity and superiority of the proposed optimization framework of multiview SAR ATR. [ABSTRACT FROM AUTHOR]

In general, autofocus methods integrated with frequency-domain imaging algorithms are instrumental to obtain a well-focused bistatic forward-looking synthetic aperture radar (BFSAR) image in the presence of motion errors. Nevertheless, before applying autofocus methods to correct the azimuth phase errors, range cell migration (RCM) should be eliminated by the RCM correction (RCMC) procedure in frequency-domain imaging algorithms. With motion errors being taken into account, there always exists some residual nonsystematic RCM (NsRCM), which refers to the residual migration components after the RCMC procedure. For the conventional side-looking SAR, NsRCM is caused by motion errors. On the other hand, NsRCM of BFSAR is originated from motion errors before RCMC and the NsRCM amplified by the RCMC procedure. In this paper, we analyze the different types of NsRCM in BFSAR imaging and their relationship. Based on the analyses, we propose an autofocus NsRCM correction scheme for BFSAR imagery using frequency-domain imaging algorithms that can eliminate the range-dependent NsRCM. The proposed scheme consists of three steps. First, for the BFSAR data after range compression and RCMC, a division procedure is carried out in the azimuth direction. The subblocks with the highest signal-to-clutter ratio along the range direction are selected after the azimuth segmentation procedure. Second, for the selected subblocks, the total NsRCM is estimated based on the minimum-entropy criterion. Based on the estimation results, different parts of the NsRCM are obtained by solving an ordinary differential equation. Third, a two-step compensation of the NsRCM is executed to reach the spatially variant correction. Simulations and experimental results are provided to demonstrate that our proposed scheme is effective for BFSAR imaging. [ABSTRACT FROM AUTHOR]

Recently, the generalized sparse iterative covariance-based estimation algorithm was extended to allow for varying norm constraints in scanning radar applications. In this paper, further to this development, we introduce a wideband dictionary framework which can provide a computationally efficient estimation of sparse signals. The technique is formed by initially introducing a coarse grid dictionary constructed from integrating elements, spanning bands of the considered parameter space. After forming estimates of the initially activated bands, these are retained and refined, whereas nonactivated bands are discarded from the further optimization, resulting in a smaller and zoomed dictionary with a finer grid. Implementing this scheme allows for reliable sparse signal reconstruction, at a much lower computational cost as compared to directly forming a larger dictionary spanning the whole parameter space. Simulation and real data processing results demonstrate that the proposed wideband estimator offers significant computational savings, without noticeable loss of performance. [ABSTRACT FROM AUTHOR]

In synthetic aperture radar (SAR) imagery, images of ground moving targets (GMTs) are smeared, distorted, and shifted. Current GMT imaging methods are mostly based on range-Doppler algorithms, which have two main drawbacks: 1) coupling between range cell migration correction (RCMC) and Doppler parameter estimation and 2) cross terms degrade the performance of the nonlinear estimation methods. In this paper, an optimal 2-D spectrum matching method for SAR GMT imaging is proposed. The main innovation or advantage of this method is that the GMT imaging problem is transformed into a constrained optimization problem, and differential evolution is applied to guarantee a high-processing efficiency. As they are associated with the Doppler centroid variation compensation and range shift compensation processing, all GMT point scatterers can be well focused and well located. Compared with the current methods, the improvements exhibited by this method include three main benefits: 1) RCMC and Doppler parameter estimation can be simultaneously accomplished; 2) both along- and cross-track GMT velocities can be simultaneously estimated; and 3) this method can be applied to both monostatic and bistatic SARs. Numerical simulations and experimental data processing have verified the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

It is a feasible and promising way to utilize deep neural networks to learn and extract valuable features from synthetic aperture radar (SAR) images for SAR automatic target recognition (ATR). However, it is too difficult to effectively train the deep neural networks with limited raw SAR images. In this paper, we propose a new approach to do SAR ATR, in which a multiview deep learning framework was employed. Based on the multiview SAR ATR pattern, we first present a flexible mean to generate adequate multiview SAR data, which can guarantee a large amount of inputs for network training without needing many raw SAR images. Then, a unique deep convolutional neural network containing a parallel network topology with multiple inputs is adopted. The features of input SAR images from different views will be learned by the proposed network layer by layer; meanwhile, the learned features from the distinct views are fused in different layers progressively. Therefore, the proposed framework is able to achieve a superior recognition performance, and requires only a small number of raw SAR images for network training samples generation. Experimental results have shown the superiority of the proposed framework based on the Moving and Stationary Target Acquisition and Recognition data set. [ABSTRACT FROM AUTHOR]

With appropriate geometry configurations, bistatic synthetic aperture radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. Owing to such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications. In BFSAR, the compensation of the spatially variant motion errors is of great significance to get a well-focused image. In this paper, first, the spatial-variance properties of motion errors are analyzed analytically and quantitatively. Different from the side-looking monostatic and bistatic SAR, 2-D space-variant motion errors should be taken into consideration in BFSAR. The 2-D spatial variance of the motion errors can be categorized into two parts, range-variant motion errors of the transmitter and azimuth-variant motion errors of the receiver. Moreover, these two parts are independent of each other. Based on this property analysis, second, a motion compensation (MoCo) approach with cubic-order processing is proposed to deal with the spatially variant motion errors in BFSAR. In the cubic-order processing, the first-order MoCo is performed to correct the spatially independent motion errors on the raw data. The second-order MoCo is accomplished on the non-range-cell-migration (RCM) data to deal with the range-variant errors. After the second-order MoCo, since the signal direction of the non-RCM data coincides with the variant direction of the uncompensated phase errors, the azimuth-variant motion errors and slow time signal are coupled together. To cope with such a problem, the slow time signal is transformed into the direction perpendicular to the azimuth by a novel procedure named azimuth–slow time decoupling. At this stage, the coupling between the azimuth-variant motion errors and slow time signal has been eliminated. Azimuth-variant motion errors can be corrected precisely. Simulation and experimental results verify the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

With the geosynchronous synthetic aperture radar (SAR) satellite as the transmitter, the unmanned aerial vehicle (UAV) can passively receive the echo within the illuminated ground area and achieve 2-D imaging of the interested target. This SAR system, known as GEO-UAV bistatic SAR, is capable of autonomously accomplishing the bistatic SAR mission in rough terrain environments by prespecifying a path for the UAV receiver. In this paper, the GEO-UAV bistatic SAR system is first investigated. The practical advantages and spatial resolution are then analyzed in detail. The spatial resolution of GEO-UAV bistatic SAR is dependent on the observation geometry, which is determined by the UAV path. Therefore, the path planning for GEO-UAV bistatic SAR aims at identifying a set of optimal paths for the UAV receiver to travel through a 3-D terrain environment that simultaneously guarantees the safety of the UAV and achieves SAR imaging with optimized performance during the flight. The path planning is modeled as a constrained multiobjective optimization problem (MOP), which accurately represents the two main aspects for the path planning problem, i.e., UAV navigation and bistatic SAR imaging. Then, a path planning method based on a constrained-adaptive-multiobjective-differential-evolution algorithm is proposed to solve the MOP and generate multiple feasible paths for the UAV receiver with different tradeoffs between navigation for UAV and bistatic SAR imaging performance. The GEO-UAV bistatic SAR mission designer can choose a path from the solution set according to the application requirements, which makes the method more pragmatic. [ABSTRACT FROM PUBLISHER]

Bistatic forward-looking synthetic aperture radar (BFL-SAR) is a kind of bistatic SAR system that can image forward-looking terrain in the flight direction of an aircraft. Until now, BFL-SAR imaging theories and methods have been researched for stationary targets. Unlike the stationary target, the motion of a ground-moving target (GMT) induces unknown range cell migration and additional modulation of the azimuth signal. Thus, to finely image the GMT, one must obtain its velocity parameters accurately, but they are usually unknown. In this paper, a novel GMT imaging and velocity estimation method, which is based on mismatched compression, is proposed for BFL-SAR without a priori knowledge of the GMT's velocity parameters. The main idea behind mismatched compression is to use a presumed azimuth reference function for performing correlated operation with the azimuth signal of the GMT. In general, the Doppler parameters of the presumed azimuth reference function are different from those of the GMT's azimuth signal because the velocity parameters of the GMT are unknown. Therefore, the correlation operation referred to earlier is actually mismatched compression, and the resulting image is shifted and defocused. The shifted and defocused image is utilized to get the real Doppler and velocity parameters of the GMT. The advantage of this method is that not only the GMT can be well focused but also the GMT's velocity can be simultaneously obtained. In addition, this method needs only monochannel antenna. The proposed BFL-SAR GMT imaging and velocity estimation method is validated by numerical simulations. [ABSTRACT FROM AUTHOR]

Geosynchronous synthetic aperture radar (GEO-SAR) offers new opportunities for continuous Earth observation missions with large coverage and short revisit cycle. The unique features of GEO-SAR present huge potentials for bistatic observation applications. In this paper, the concept and advantages of GEO bistatic SAR (GEO-BiSAR) are first investigated. The system consists of a GEO illuminator and an airborne receiver, such as an airplane or a near-space vehicle. Compared with a monostatic GEO-SAR system, the bistatic configuration can provide finer spatial resolution and higher signal-to-noise ration (SNR) with less system complexity. The spatial resolution characteristics are then analyzed based on generalized ambiguity function, where the time-varying GEO velocity, Earth rotation, and ellipsoid Earth surface are taken into consideration. Meanwhile, the bistatic SNR is analyzed using the integration equation model. In this paper, the mission design for GEO-BiSAR aims at identifying a set of receiver flight parameters and bistatic configurations to obtain the desired spatial resolution and SNR. Based on the desired imaging performance of a specific application background, the mission design process is modeled as a nonlinear equation system (NES). Finally, a mission design method based on fast nondominated sorting genetic algorithm is proposed to solve the NES and obtain multiple optimal solutions to guide the receiver flight missions. Examples of the mission design process are given to validate the effectiveness of the proposed method. The results of the mission design can be conveniently used to guide the receiver flight mission for the desired imaging performance, which is highly desirable in practical applications. [ABSTRACT FROM AUTHOR]