An Inverse Extended Omega-K Algorithm for SAR Raw Data Simulation With Trajectory Deviations

An efficient and accurate inverse extended omega-K algorithm (IEOKA) is proposed to simulate synthetic aperture radar (SAR) raw data with trajectory deviations. Different from the traditional inverse omega-K algorithm that assumes an ideal flight trajectory, the IEOKA not only recovers the range cell migration accurately but also considers the motion errors including both range and phase errors due to the use of inverse extended Stolt interpolation. Furthermore, the azimuth dependence of the motion errors is discussed. A beam division method based on frequency division technique is presented to generate the azimuth-dependent phase error more accurately. The accuracy and effectiveness of the proposed algorithm have been verified using the generated SAR raw data consisting of the azimuth-dependent motion error.