Synthetic Aperture Radar Raw Data Simulation Research Articles

A Novel High-Squint Spotlight SAR Raw Data Simulation Scheme in 2-D Frequency Domain

Raw data simulation is the front-end work of synthetic aperture radar (SAR), which is of great significance. For high-squint spotlight SAR, the frequency domain simulation algorithm is invalid because of the range-azimuth coupling effect. In order to realize high-squint spotlight SAR raw data simulation in the frequency domain, an algorithm based on coordinate transformation and non-uniform fast Fourier transform (NUFFT) is proposed. This algorithm generates broadside raw data using a two-dimensional (2-D) frequency simulation algorithm; then, coordinate transformation is used by analyzing the characteristics of broadside and high-squint spotlight SAR. After coordinate transformation, NUFFT is carried out to realize the coupling relation in the 2-D frequency domain. Since the coordinate transformation ignores the influence of range walk, the range walk is compensated after NUFFT. As a result, compared with the traditional squint spotlight SAR frequency domain simulation algorithm, the proposed algorithm can improve the accuracy of point and distributed target imaging results, and the efficiency of the proposed algorithm can be significantly improved in contrast the traditional time domain algorithm.

SAR Raw Data Simulation for Fluctuant Terrain: A New Shadow Judgment Method and Simulation Result Evaluation Framework

Synthetic aperture radar raw data simulation (SAR-RDS) is beneficial to the SAR system design, signal processing method verification, and radar parameter optimization. Most SAR-RDS methods are based on the flat terrain assumption. However, the fluctuant terrain in real scene will induce severe SAR beam occlusion effect and produce radar shadow, leading to incorrect RDS results. Thus, a dynamic elevation angle interpolation (DEAI) algorithm is proposed for SAR shadow judgment by considering the actual SAR working process. The key of the proposed DEAI algorithm is the 1-D EAI and shadow visualization update, which avoids the problem that the existing methods cannot judge the shadow of partial areas due to the insufficiently refined mesh grid or the mismatch of the judgment model. Moreover, an evaluation framework named as joint image and signal criteria (JISC) is proposed from the perspectives of SAR imaging and signal processing results to objectively evaluate the SAR-RDS results and solve the problem that the existing evaluation methods cannot be compatible with fluctuant terrain. Finally, the numerical experiment verified our theoretical analyses.

Analysis of the Multipath Scattering Effects in High-Resolution SAR Images

Due to multipath (MP) scattering, many special phenomena and details about the targets in high-resolution synthetic aperture radar (SAR) images are hard to understand. In this letter, a more elaborate improved model combined with the synthetic aperture progress is presented, to analyze the MP scattering. We explore the defocusing and displacement phenomena of MP scattering through signal modeling and time–frequency relationship analysis. The theoretical equations for the displacement of double scattering are derived, and the equation of phase error which causes the defocusing is also given. The SAR raw data simulation of MP scattering and range-Doppler (RD) imaging results validates the correctness of the analysis. From the equations, the effects of MP scattering of different height and different orientation angle in high-resolution SAR images are given. Finally, based on the simulation results, the new interpretation about the relationship between the moustache effect in high-resolution TerraSAR-X SAR images and the height of the structure is obtained.

Multiple Mode SAR Raw Data Simulation and Parallel Acceleration for Gaofen-3 Mission

Gaofen-3 is China's first meter-level multipolarization synthetic aperture radar (SAR) satellite with 12 imaging modes for the scientific and commercial applications. In order to evaluate the imaging performance of these modes, the multiple mode SAR raw data simulation is highly demanded. In the paper, the multiple mode SAR simulation framework will be briefly introduced to expose how the raw data simulation guarantees the development of Gaofen-3 and its ground processing system. As an engineering simulation, the complex working modes and practical evaluation requirements of Gaofen-3 mission put forward to the higher demand for simulation simplification and data input/output (I/O) efficiency. To meet the requirements, two improvements have been proposed. First, the stripmap mode based multiple mode decomposition method is introduced to make a solid and simplified system simulation structure. Second, the cloud computing and graphics processing unit (GPU) are integrated to simulate the practical huge volume raw data, resulting in improved calculation and data I/O efficiency. The experimental results of sliding spotlight imaging prove the effectiveness of the Gaofen-3 mission simulation framework and the decomposition idea. The results for efficiency assessment show that the GPU cloud method greatly improves the computing power of a 16-core CPU parallel method about $40\times$ speedup and the data throughput with the Hadoop distributed file system. These results prove that the simulation system has the merits of coping with multiple modes and huge volume raw data simulation and can be extended to the future space-borne SAR simulation.

An Integrated Method for Simulation of Synthetic Aperture Radar (SAR) Raw Data in Moving Target Detection

SAR (Synthetic Aperture Radar) raw data simulation has a significant role in the evaluation of newly-proposed methods for the estimation of moving target parameters. The evaluation of methods in different cases emphasizes the importance of the need to have fast simulators. Using reverse SAR imaging methods for the raw data simulations has achieved good results in the simulation of the static targets, but for the simulation of moving targets these methods have a few shortcomings. In this paper, we propose a method to simulate a speckled scene with moving targets in the hybrid domain. First, the scene is simulated, including speckle, which is statistically in accordance with real SAR image behavior. Then, a reverse imaging algorithm (inverse chirp scaling) was applied on the scene to generate the SAR raw data. The moving target simulation was also done in the time-domain as the next step. Finally, the results from two prior steps were superposed to generate the SAR raw data with moving targets. All steps of the proposed method were evaluated separately. The speckle procedure was evaluated by comparing the speckled SAR image before the simulation and the image of the SAR simulated raw data. The results show similar variations in real and imaginary parts of these data. The correlation between the reflectivity map and the SAR images after the simulation was calculated and the obtained correlation coefficient was about 0.95 for different images. The final data were further analyzed for the displacement of moving targets’ positions. The results show similar displacement between moving target SAR raw data with a background and without a background.

A Deep Collaborative Computing Based SAR Raw Data Simulation on Multiple CPU/GPU Platform

The outstanding computing ability of a graphics processing unit (GPU) brings new vitality to the typical computing intensive issue, so does the synthetic aperture radar (SAR) raw data simulation, which is a fundamental problem in SAR system design and imaging research. However, the computing power of a CPU was underestimated, and the tunings for a CPU-based method were missing in the previous works. Meanwhile, the collaborative computing of multiple CPUs/GPUs was not exploited thoroughly. In this paper, we propose a deep multiple CPU/GPU collaborative computing framework for time-domain SAR raw data simulation, which not only introduces the advanced vector extension (AVX) method to improve the computing efficiency of a multicore single instruction multiple data CPU, but also achieves a satisfactory speedup in the CPU/GPU collaborative simulation by fine-grained task partitioning and scheduling. In addition, an irregular reduction based SAR coherent accumulation approach is proposed to eliminate the memory access conflict, which is the most difficult issue in the GPU-based raw data simulation. Experimental results show that the multicore vector extension method greatly improves the computing power of a CPU-based method through about 70× speedup, thereby outperforming the single GPU simulation. Correspondingly, compared with the baseline sequential CPU approach, the multiple CPU/GPU collaborative simulation achieves up to 250× speedup. Furthermore, the irregular reduction based atomic-free optimization boosts the performance of the single GPU method by 20% acceleration. These results prove that the deep multiple CPU/GPU collaborative method is promising, especially for the case of huge volume raw data simulation with a wide swath and high resolution.

A Fast Synthetic Aperture Radar Raw Data Simulation Using Cloud Computing.

Synthetic Aperture Radar (SAR) raw data simulation is a fundamental problem in radar system design and imaging algorithm research. The growth of surveying swath and resolution results in a significant increase in data volume and simulation period, which can be considered to be a comprehensive data intensive and computing intensive issue. Although several high performance computing (HPC) methods have demonstrated their potential for accelerating simulation, the input/output (I/O) bottleneck of huge raw data has not been eased. In this paper, we propose a cloud computing based SAR raw data simulation algorithm, which employs the MapReduce model to accelerate the raw data computing and the Hadoop distributed file system (HDFS) for fast I/O access. The MapReduce model is designed for the irregular parallel accumulation of raw data simulation, which greatly reduces the parallel efficiency of graphics processing unit (GPU) based simulation methods. In addition, three kinds of optimization strategies are put forward from the aspects of programming model, HDFS configuration and scheduling. The experimental results show that the cloud computing based algorithm achieves speedup over the baseline serial approach in an 8-node cloud environment, and each optimization strategy can improve about 20%. This work proves that the proposed cloud algorithm is capable of solving the computing intensive and data intensive issues in SAR raw data simulation, and is easily extended to large scale computing to achieve higher acceleration.

SAR Raw Data Simulation for Ocean Scenes Using Inverse Omega-K Algorithm

This paper deals with synthetic aperture radar (SAR) raw data simulation for ocean scenes featuring surface waves and currents, an issue which has proven to be of great necessity in preparing for future oceanic SAR missions. In this paper, the inverse Omega-K (IOK) algorithm, which is originally designed for SAR raw data simulation of stationary land scenes, is extended to ocean scenes. To realize such a generalization, endeavors are made in two aspects. First, specially aimed at ocean dynamics of ocean waves and currents, the 2-D spectrum of the SAR signal is derived. Second, to account for the spatial variation of ocean-motion parameters, we adopt a strategy called batch processing, whose basic feature is that a single implementation of the IOK algorithm will simultaneously simulate a collection of ocean-surface backscattering elements that have the same radial velocity. For the proposed simulator, the velocity bunching effect is embodied via making the long-wave radial orbital velocities physically enter the range equation of the SAR raw signal, instead of superimposing this effect onto the reflectivity map. The spread of the facet velocities within one resolution cell enters the raw data through a random perturbation of the local long-wave orbital velocity. The proposed simulator is not only rather accurate due to the fact that, in deriving the range frequency mapping function, no Taylor expansion is made on the range equation, but also much more efficient than its time-domain counterpart. Effectiveness of the proposed simulator is validated by using simulation results.

Accelerating Time-Domain SAR Raw Data Simulation for Large Areas Using Multi-GPUs

Large areas synthetic aperture radar (SAR) raw data simulation, which contains various actual system errors, is one of the main tasks in SAR system design and development. The growth of swath and resolution results in a significant increase in data volume as well as the simulation time. This poses a challenge for SAR raw data simulation considering system errors. For recent years, the graphics processing unit (GPU)-based scalable parallel method has been applied to raw data simulation. In this paper, we investigate time-domain SAR raw data simulation for large areas on multi-GPUs architecture, which can not only simulate raw data of large areas by task partitioning and scheduling, but also improve the efficiency of current GPU-based algorithm by access conflict optimization and fine-grained parallel pipeline. Experimental results show that the proposed multi-GPUs-based raw data simulation method achieves a 5× speedup compared to the current GPU-based method on single GPU, and a significant over 500× speedup on 4 GPUs compared to traditional CPU-based simulation. These results verify that multi-GPUs-based time-domain method is very suitable for large data volume raw data simulation, especially for the case of wide swath and high resolution.

Time Domain SAR Raw Data Simulation of Distributed Targets

In this paper, a time domain stripmap mode Synthetic Aperture Radar (SAR) raw data simulation including both the terrain and the targets is proposed. The simulator generates SAR raw data of a scene, involving both single and double reflections in a computationally efficient manner. The inputs of the simulator are the digital elevation model of the terrain, the 3D target model, and the parameters of the SAR system. The simulator extracts a geometrically accurate reflectivity map and generates the SAR raw data in time domain. The disadvantage of time domainmethod is justified to be tolerable by presenting experiments onmodularity performance of the simulator. Also a novel method to decrease the time domain computational complexity of the SAR raw data generation is proposed. Our method has showed very promising results in representing the scattering characteristics, the raw data, and the time domain simulation flexibility.