Synthetic Aperture Radar Imaging Performance Research Articles

Terminal Trajectory Planning for Synthetic Aperture Radar Imaging Guidance Based on Chronological Iterative Search Framework.

Synthetic aperture radar (SAR) is capable of obtaining the high-resolution 2-D image of the interested target scene, which enables advanced remote sensing and military applications, such as missile terminal guidance. In this article, the terminal trajectory planning for SAR imaging guidance is first investigated. It is found that the guidance performance of an attack platform is determined by the adopted terminal trajectory. Therefore, the aim of the terminal trajectory planning is to generate a set of feasible flight paths to guide the attack platform toward the target and meanwhile obtain the optimized SAR imaging performance for enhanced guidance precision. The trajectory planning is then modeled as a constrained multiobjective optimization problem given a high-dimensional search space, where the trajectory control and SAR imaging performance are comprehensively considered. By utilizing the temporal-order-dependent property of the trajectory planning problem, a chronological iterative search framework (CISF) is proposed. The problem is decomposed into a series of subproblems, where the search space, objective functions, and constraints are reformulated in chronological order. The difficulty of solving the trajectory planning problem is thus significantly alleviated. Then, the search strategy of CISF is devised to solve the subproblems successively. The optimization results of the preceding subproblem can be utilized as the initial input of the subsequent subproblems to enhance the convergence and search performance. Finally, a trajectory planning method is put forward based on CISF. Experimental studies demonstrate the effectiveness and superiority of the proposed CISF compared with the state-of-the-art multiobjective evolutionary methods. The proposed trajectory planning method can generate a set of feasible terminal trajectories with optimized mission performance.

Motion Estimation and Improved SAR Imaging for Agile Platforms Using Omnidirectional Radar and INS Sensor Fusion

Accurate motion estimation is a challenging problem for agile radar platforms, even when state-of-the-art inertial navigation sensors (INSs) are used. However, it is an important problem to solve as it can have a large impact on the performance of radar modes, such as synthetic aperture radar (SAR). This study addresses the motion estimation problem of agile radar platforms from the perspective of an omnidirectional radar array. In this study, we perform an analysis on the applicability of an omnidirectional radar array to explicitly estimate the motion of an agile SAR platform and improve imaging quality. Building on existing 1-D SAR motion compensation techniques, we develop a method to estimate the 3-D motion of the radar platform utilizing its height and velocity vector. Using a prototype radar developed at the Netherlands Organisation for Applied Scientific Research, we experimentally verify that using the proposed velocity estimation method alone, we achieve comparable positioning performance to that of a state-of-the-art INS, making it possible to perform INS-free SAR imaging by using arbitrary flight paths. We also show that fusing the radar positioning estimates obtained from the proposed methods with the INS output yields an additional increase in SAR imaging performance, improving the resolvability and detectability of weak targets.

Mission Planning for Energy-Efficient Passive UAV Radar Imaging System Based on Substage Division Collaborative Search.

In our earlier study, an energy-efficient passive UAV radar imaging system was formulated, which comprehensively analyzed the system performance. In this article, based on the evaluator set, a mission planning framework for the underlying energy-efficient passive UAV radar imaging system is proposed to achieve optimized mission performance for a given remote sensing task. First, the mission planning problem is defined in the context of the proposed synthetic aperture radar (SAR) system and a general framework is outlined, including mission specification, illuminator selection, and path planning. It is found that the performance of the system is highly dependent upon the flight path adopted by the UAV platform in a 3-D terrain environment, which offers the potential of optimizing the mission performance by adjusting the UAV path. Then, the path planning problem is modeled as a single-objective optimization problem with multiple constraints. Path planning can be divided into two substages based on different mission orientations and low mutual correlation. Based on this property, a path planning method, called substage division collaborative search (Sub-DiCoS), is proposed. The problem is divided into two subproblems with the corresponding decision space and subpopulation, which significantly relax the constraints for each subproblem and facilitates the search for feasible solutions. Then, differential evolution and the whole-stage best guidance technique are devised to cooperatively lead the subpopulations to search for the best solution. Finally, simulations are presented to demonstrate the effectiveness of the proposed Sub-DiCoS method. The result of the mission planning method can be used to guide the UAV platform to safely travel through a 3-D rough terrain in an energy-efficient manner and achieve optimized SAR imaging and communication performance during the flight.

Parameter Design and Imaging Method of Spaceborne Azimuth Interrupted FMCW SAR

Spaceborne azimuth interrupted frequency modulation continuous wave (AI-FMCW) synthetic aperture radar (SAR), known as a novel system to observe the characteristics of ground objects, employs a single antenna to transmit and receive continuous waves, thus different from traditional frequency modulated continuous wave (FMCW) SAR that needs two antennas. However, due to the single antenna, there is a phenomenon that the transmitting of several continuous waves could be unavailable while replaced by receiving radar echoes. The process of switching the transmitter ON and OFF will leave gaps in the synthetic aperture. In this case, traditional imaging algorithms such as the wavenumber domain algorithm (WDA) cannot acquire clear focused images, resulting in the blurred ones corrupted by ghost targets. To solve the problem mentioned above, this letter presents an image restoration algorithm to improve the AI-FMCW SAR image performance. In this letter, there are two main contributions: 1) some key parameters of the AI-FMCW SAR system are designed and 2) a method based on Richardson–Lucy (RL) deconvolution algorithm is applied to AI-FMCW SAR imaging to suppress ghost targets. Finally, a case of the Ku-band AI-FMCW SAR system is given, and experiments via simulated AI-FMCW SAR data are conducted to validate the proposed method.

Apsidal Precession Effects on the Lunar-Based Synthetic Aperture Radar Imaging Performance

There have been considerable interests in the lunar-based synthetic aperture radar (LBSAR) for monitoring large-scale geoscience phenomena. However, the signal distortions given rise by lunar orbital perturbations, especially the apsidal precession, are particularly severe in the LBSAR. The apsidal precession effects can induce a coordinate drift of the LBSAR, which can further lead to the variation in the range history of the LBSAR. As a result, LBSAR’s image performance might be affected. In this letter, we thoroughly investigate whether the apsidal precession effects cause the phase decorrelation in the signal of the LBSAR, and how such effects impact the LBSAR imaging. The theoretical result shows that the impact of the lunar apsidal precession mainly results in the first-order and second-order Doppler errors, which further influence the geometric location and focusing quality along the azimuth direction. Numerical simulations using the point target response show good consistency with the theoretical analysis. To this end, the lunar apsidal precession effects deserve special care in the LBSAR for high imaging quality.

$L_{1}$ -Regularization-Based SAR Imaging and CFAR Detection via Complex Approximated Message Passing

Synthetic aperture radar (SAR) is a widely used active high-resolution microwave imaging technique that has all-time and all-weather reconnaissance ability. Compared with traditionally matched filtering (MF)-based methods, ${L_{q}}({0 \le q \le 1})$ regularization technique can efficiently improve SAR imaging performance e.g., suppressing sidelobes and clutter. However, conventional $L_{q}$ -regularization-based SAR imaging approach requires transferring the 2-D echo data into a vector and reconstructing the scene via 2-D matrix operations. This leads to significantly more computational complexity compared with MF, and makes it very difficult to apply in high-resolution and wide-swath imaging. Typical $L_{q}$ regularization recovery algorithms, e.g., iterative thresholding algorithm, can improve imaging performance of bright targets, but not preserve the image background distribution well. Thus, image background statistical-property-based applications, such as constant false alarm rate (CFAR) detection, cannot be applied to regularization recovered SAR images. On the other hand, complex approximated message passing (CAMP), an iterative recovery algorithm for $L_{1}$ regularization reconstruction, can achieve not only the sparse estimation of the original signal as typical regularization recovery algorithms but also a nonsparse solution simultaneously. In this paper, two novel CAMP-based SAR imaging algorithms are proposed for raw data and complex radar image data, respectively, along with CFAR detection via the CAMP recovered nonsparse result. The proposed method for raw data can not only improve SAR image performance as conventional $L_{1}$ regularization technique but also reduce the computational cost efficiently. While only when we have MF recovered SAR complex image rather than raw data, the proposed method for complex image data can achieve a similar reconstructed image quality as the regularization-based SAR imaging approach using the full raw data. The most important contribution of this paper is that the proposed CAMP-based methods make CFAR detection based on the regularization reconstruction SAR image possible using their nonsparse scene estimations, which has a similar background statistical distribution as the MF recovered images. The experimental results validated the effectiveness of the proposed methods and the feasibility of the recovered nonsparse images being used for CFAR detection.

Azimuth-range decouple-based L 1 regularization method for wide ScanSAR imaging via extended chirp scaling

In this paper, we proposed an azimuth-range decouple-based L1 regularization method for wide ScanSAR imaging via extended chirp scaling (ECS) and applied it to the TerraSAR-X data to achieve large-scale sparse reconstruction. Compared with ECS, the conventional ScanSAR imaging algorithm based on matched filtering, the proposed method can improve the synthetic aperture radar image performance with full-sampling raw data for not only sparse but also nonsparse surveillance regions. It can also achieve high-resolution imaging for sparse considered scenes efficiently using down-sampling raw data. Compared with a typical L1 regularization imaging approach, which requires transfer of the two-dimensional (2-D) echo data into a vector and reconstruction of the scene via 2-D matrix operation, our proposed method has less computational cost and hence makes the large-scale regularization reconstruction of considered area become possible. The experimental results via real data validate the effectiveness of the proposed method.

An Approach for Refocusing of Ground Moving Target Without Target Motion Parameter Estimation

In synthetic aperture radar (SAR), long integration time may induce range migration and Doppler frequency migration of a received signal, which may degrade the SAR imaging performance of ground moving targets. Most of the conventional algorithms deal with the problems of range migration and Doppler frequency migration based on parameter searching. However, the exhaustive searching of target motion parameters may result in heavy computational burden. To avoid this problem, this paper proposes a new imaging method for ground moving targets without target motion parameter estimation. First, Keystone transform is applied to correct the range walk. Second, range curvature is compensated by the matched filtering function. Third, Doppler frequency migration is compensated via multiplying the data in range- and azimuth-time domains by its reversed conjugate data according to the equal interval sampling of the azimuth slow time, which avoids the searching procedure for target motion parameter estimation. Finally, the signal energy will be well accumulated in the range–Doppler domain, and thus, the moving targets can be efficiently recognized in the focused image. The major advantage of the proposed method is that it can obtain well-focused images of all targets in one processing step without target motion parameter estimation; thus, it is computationally efficient. Both simulated and real data processing results are used to validate the effectiveness of the proposed method.

Path Planning for GEO-UAV Bistatic SAR Using Constrained Adaptive Multiobjective Differential Evolution

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.

Synthetic Aperture Radar image nonlinear enhancement algorithm based on NSCT transform

Due to the imaging mechanism, Synthetic Aperture Radar (SAR) images are susceptible to speckle noise, which affects radar image interpretation. So image denoising and enhancement are important topics of improving SAR image performance. A nonlinear image enhancement algorithm based on nonsubsampled contourlet transform (NSCT) is proposed in this paper. The image is decomposed into coefficients of different scales and directions through nonsubsampled contourlet transform. It is denoised by the threshold method of the multi-scale product of NSCT coefficients. Then thresholds of the nonlinear enhancement function are determined according to the coefficients of each scale. The two parameters of the function, among which one is used to control the range of enhancement and the other can determine the strength of enhancement, are obtained by solving nonlinear equations. The coefficients processed by the enhancement function are used to reconstruct the image. The simulation results on the Matlab platform show that the algorithm has a good effect of enhancing details of images and suppressing noise signals meanwhile.

Multiband Microwave Imaging Analysis of Ionosphere and Troposphere Refraction for Spaceborne SAR

Ionosphere has different stratification at the different height. Troposphere has different refractivity at the different height. When microwave signals transmit through the ionosphere and the troposphere, the real propagation path is not an ideal straight line, but a slightly curved straight line. For the synthetic aperture radar (SAR) system, the actual distance errors will result in phase errors, which impact range section and azimuth section of SAR raw data. Consequently, the imaging precision has been decreased by imprecise slant range history. In this paper, we simulate the propagation path between satellite and the target according to Snell's law and analyze how the ionospheric and tropospheric refraction impact the spaceborne SAR imaging performance at L-band and X-band. The simulation results show that the two refraction effects should be compensated in low frequency band for better image focusing performance.

RMA를 이용한 Spotlight SAR 영상형성 및 품질평가를 위한 시뮬레이터 개발 및 구현

합성 개구면 레이다(SAR: synthetic aperture radar)는 날씨 및 주/야간에 관계없이 고해상도의 영상을 형성할 수 있기 때문에 다양한 분야에서 활용되고 있다. 본 논문에서는 영상 형상 및 품질 평가, 분석을 위한 Spotlight SAR 시뮬레이터를 제안한다. SAR 영상 품질의 예측은 초기 개념 설계 단계부터 최종적인 비행 검증 및 보정 단계에 걸쳐 필수적이다. 제안된 SAR 시뮬레이터는 시스템 설계 변수들에 기반을 두고 있어 전체 시스템 개발과정에서 SAR 영상의 품질을 예측 분석할 수 있다. 이를 위해 영상 획득 기하구조와 다양한 SAR 시스템 설계변수들을 고려하여 이상적인 점표적에 대한 시뮬레이션 원시 데이터를 생성하고 Spotlight 영상 형성 알고리듬을 구현함으로서 점표적 응답특성을 추정한다. 마지막으로 공간 해상도, 최대 부엽 수준, 누적 부엽 수준 등의 주요 품질변수들을 중심으로 생성된 원시 데이터의 영상 품질을 분석한다. Synthetic aperture radar (SAR) is widely used because of high resolution imaging capability in all weather and day/night condition. In this paper development of Spotlight SAR simulator is proposed for image quality analysis. Proposed SAR simulator is based on the SAR system design parameters so that SAR image performance can be expected which is essential throughout the full system development procedure from the initial concept design stage to the final in-flight calibration and validation stage. The raw data of ideal point target is first generated by taking account of the flight and imaging geometry and the various SAR system design parameters, and the Spotlight image formation algorithm is implemented in order to obtain the point target response. Finally the image quality of the generated raw data is analyzed in terms of spatial resolution, peak to sidelobe ratio and integrated sidelobe ratio.

Degrading effects of the lower atmosphere on long-range airborne synthetic aperture radar imaging

The imaging performance of airborne synthetic aperture radar (SAR) systems has advanced to the point that the effects of clear-air refractive index perturbations cannot be ignored. Operating at long ranges, and low grazing angles, in particular, require propagation geometries through regions of the lower atmosphere that may cause noticeable and, sometimes, severe degradation of the images. The range of image anomalies that can be attributed to the atmospheric boundary layer (ABL) is illustrated, the pertinent characteristics of the ABL is discussed, a first-order SAR imaging model that incorporates the refractive index perturbations associated with the ABL is developed and the magnitude of the image anomalies resulting from measured refractive index perturbations is estimated. The model predictions correlate well with the observed image anomalies and measured properties of the ABL. On the basis of theory and measurements, it is expected that the degrading effect of clear-air atmospheric refractive index perturbations is much more common than previously thought and may be a limiting factor for long-range SAR imaging performance.