Focusing high-squint and large-baseline one-stationary bistatic SAR data using keystone transform and enhanced nonlinear chirp scaling based on an ellipse model
With appropriate geometry configurations, bistatic synthetic aperture radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. Thanks to such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications, such as self-navigation and self-landing. In the mode of BFSAR with a stationary transmitter (ST-BFSAR), the two-dimensional spatial variation makes it difficult to use traditional data focusing algorithms. In this letter, an imaging algorithm based on keystone transform and nonlinear chirp scaling (NLCS) is proposed to deal with this problem. Keystone transform is used to remove the spatial variation of range cell migration. NLCS can eliminate the variation of azimuth reference function. Numerical simulations show that by combining firstorder keystone transform and azimuth NLCS operation, the raw data of ST-BFSAR can be well imaged.