Refocusing of moving targets in spotlight sar raw data

Abstract

Synthetic Aperture Radar (SAR) requires specialized image formation Algorithms, which has capabilities like detection of moving targets, and highly focused vehicle identification. However, when it comes to large image scenes, the computational cost increases significantly. Basically, there are two approaches to solve large image formation issue. First technique is to use the exact algorithms like Match filter and Backprojection Algorithm (BPA). In this technique the calculation is done individually in every pixel and every pulse which requires a high performance of computing. The advantage of this technique is the new recorded samples can be integrated easily and there is also no phase error. Second technique uses the fast image algorithms like Polar Format Algorithm (PFA). But this technique introduces geometric dislocation of the target and the defocusing target artifacts when they are located away from the scene center. In literature two approaches is mainly used to minimize the effect of these image artifacts: In the first approach the large scene is divided into small patches and each patch (sub-image) is processed relatively to patch scene center instead of using the whole scene center. At the second approach instead of dividing smaller image subpatches, the phase corrections are applied after the image formation. A new target-refocusing technique based-on re-centering phase computation of previously recorded moving target raw data is implemented to the Spotlight SAR data in order to obtain refocused moving targets. The technique is tested on the integrated simulated data; background real spotlight SAR Raw data with the synthetically generated data domes of civilian moving targets. First Polar Format Algorithm is applied to detect and estimate the speed of ground-moving targets on the integrated raw data. At the next step, re-organize the integrated raw data by selecting and arranging target focusing center with a new technique based on re-centering phase computation to each moving target speed. At the third step re-organize the raw data by re-centering the phase computation to each moving target location. Finally, Polar Format Algorithm is applied to each re-organized raw data to obtain highly focused moving targets individually. To evaluate the performance of the proposed algorithm two parameters are considered. The overall image quality of the focused target and the blurrness metric of the focused target. The mean square error (MSE) and peak signal-to-noise (PSNR) ratio is used to evaluate the image quality and variation of the Laplacian is used to compute the blurriness metric. Proposed algorithm result on the moving targets is compared with the conventional PFA result on the stationary targets. By using the %100 accuracy on the target velocity estimation the proposed algorithm gives smaller MSE values for moving targets comparing even with the stationary target results using conventional PFA. Highest variation of the Laplacian values is also achieved by using the proposed algorithm, meaning defocusing artifacts are minimized comparing with even the stationary target conventional PFA result. Proposed algorithm performance is also tested by using different target velocity estimation accuracy. For this purpose 95%, 90% and 85% velocity estimation accuracies are used. The results show that algorithm performance is still better in sense of image quality (lower MSE and higher PSNR) even using the lower estimation accuracies. However velocity estimation error causing smeaiing which leads the final target image blurry. But it is also shown that by using the 95% velocity estimation accuracy variance of Laplacian value of the focused target is close to the stationary target result where is located away from the scene center. For smaller targets (less scatterer from the scenario) the effects will be less visible but the proposed algorithm will still give the better performance for both visually and for quality evaluation parameters perspective by using the higher velocity estimation accuracy results.