结合MD自聚焦算法与回波模拟算子的快速稀疏微波成像误差补偿算法

稀疏微波成像是将稀疏信号处理理论引入微波成像中,利用系统的稀疏约束突破传统合成孔径雷达(SAR)成像中系统复杂度的瓶颈,是微波成像的新理论、新体制和新方法。在传统的机载SAR成像中都会面临非理想运动带来的回波相位误差问题,可通过基于回波数据的自聚焦算法加以解决;但在机载稀疏微波成像中,因稀疏微波成像采用稀疏重建算法取代了传统SAR中基于匹配滤波的信号处理方法,传统的基于回波数据的自聚焦算法难以直接应用。现有基于稀疏重建的自聚焦算法主要基于两步迭代方法,收敛速度慢、运算量大。该文以基于回波模拟算子的快速稀疏微波成像算法为基础,将子孔径相关(MD)自聚焦算法引入,与之结合构建了新的MD-回波模拟算子自聚焦算法。该方法继承了基于回波模拟算子算法快速重建的优势,并利用MD自聚焦算法实现了回波2次相位误差的正确补偿,与现有基于两步迭代的稀疏微波成像自聚焦算法相比,收敛速度快,并可以实现较好的自聚焦效果。 

Accelerated Sparse Microwave Imaging Phase Error Compensation Algorithm Based on Combination of SAR Raw Data Simulator and Map-drift Autofocus Algorithm

Sparse microwave imaging is new concept, theory and methodology of microwave imaging, which introduces the sparse signal processing theory to microwave imaging and combines them together to overcome the paradox of increasing system complexity and imaging performance of current Synthetic Aperture Radar (SAR) systems. Traditional airborne SAR systems are facing a phase error problem in the echo which is caused by the non-ideal motion of the aircraft. This phase error could be compensated by autofocus algorithms. But in the sparse microwave imaging, such autofocus algorithm are no longer valid because traditional signal processing based on matched filtering has been replaced with sparse reconstruction. Current autofocus algorithms under sparse constraints are usually based on a two-step iteration, which convergences slowly and costs plenty of computation. In this paper, we introduce the Map-Drift (MD) autofocus algorithm to the accelerated sparse microwave imaging algorithm based on SAR raw data simulator, and propose the novel MD-SAR raw data simulator autofocus algorithm. This algorithm keeps the advantages of both accelerated imaging algorithm and MD algorithm, including the fast convergence and accurate compensation of two-order phase error in echo. Compared with current algorithms based on two-step iteration, the propose method convergences fast and effectively.