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

Abstract

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.

Refocusing of Ground Moving Targets with Doppler Ambiguity Using Keystone Transform and Modified Second-Order Keystone Transform for Synthetic Aperture Radar

“…where "←" indicates the azimuth-time reversing process in the range-frequency domain and s 2 ( f , [30][31][32]. After the parameter separation operation is performed to Equation (9), the coupling between first-order term and second-order term is effectively removed. In accordance with Equation (10), the effect of the first-order term is accurately eliminated.…”

“…A number of moving targets inevitably appear in the observation scene. Therefore, the refocusing of ground moving targets has become an important function for SAR systems due to the huge demand for the monitoring of moving targets [7][8][9][10][11][12][13][14].…”

Automatic Target Recognition for Synthetic Aperture Radar Images Based on Super-Resolution Generative Adversarial Network and Deep Convolutional Neural Network

“…Due to its advantages of all-day, all-weather, and strong penetrating capability, synthetic aperture radar (SAR) has been widely used in military and civil fields. SAR is a kind of active microwave imaging radar, which can obtain two-dimensional (2-D) images with high resolution [1][2][3][4]. The automatic target recognition (ATR) is for SAR images to extract stable and iconic features based on SAR images, and determine its category attribute and confirm its particular copies of the same class, which can be applied to battlefield monitoring, guidance attack, attack effect assessment, marine resource detection, environmental geomorphology detection, and natural disaster assessment, and has vital research significance.…”

Efficient Algorithm for SAR Refocusing of Ground Fast-Maneuvering Targets

“…SAR has been widely used in numerous remote sensing applications, such as marine observation, traffic monitoring and antiterrorism. The growing demand for surveillance of moving targets has made imaging these targets a major task for modern SAR systems [11][12][13][14][15]. Nevertheless, the unknown motion parameters between the SAR platform and the moving target result in range cell migration (RCM) and Doppler frequency migration (DFM) [16,17].…”