Synthetic Aperture Radar autofocus via Semidefinite Relaxation

Synthetic Aperture Radar (SAR) imaging can suffer from image focus degradation due to unknown platform or target motion. Autofocus algorithms use signal processing techniques to remove the undesired phase errors. The recently proposed multichannel autofocus models formulate the problem as the solution to Ae^jφ ≈ 0, where A is a given matrix and φ are the unknown phases. Previous methods approximated ejf using the null vector of A. We propose to approximate e^jφ using conic optimization and call this new autofocus algorithm Semidefinite Relaxation Autofocus (SDRA). Experimental results using a simulated SAR image shows that SDRA has promising performance advantages over existing autofocus methods.