A Kalman Filter Approach to Airborne SAR Motion Compensation Integrated with the Chirp Scaling Algorithm

In this paper, we consider a motion compensation scheme using Kalman filtering to airborne Synthetic Aperture Radars (SAR). The parameters to be estimated include sensor positions and sensor attitudes. We will show that Kalman filtering is superior to extract motion parameters compared with traditional methods. In addition, the particle filter may further take advantages to nonlinear Gaussian issues. Based on the parameters by a tracking filter, the Chirp Scaling algorithm integrated with motion compensation was implemented to generate target impulse response. It may provide good results to motion errors in both cross track and along track. Computer simulations demonstrate that Kalman filtering is computationally efficient and numerically accurate to airborne SAR motion compensation.