Bistatic synthetic aperture radar (BSAR) is an imaging technique that uses geometric and frequency diversity to resolve targets in a stationary scene. Imaging performance is described by the point spread function (PSF) and the associated ambiguity function (AF). The PSF is determined by the BSAR collection geometry and spectral content of the transmitted wave-form. For a given BSAR collection geometry, there are innite possible PSFs. Conversely for a given PSF, there are innite BSAR collection geometries that are capable of supporting the given PSF. The objective of this study is to develop an invertible model that can describe coherent imaging performance for any BSAR collection geometry and can determine a set of supporting geometries for a given imaging performance. It is shown that the ideal domain to describe imaging performance analysis and conduct geometry synthesis is a spatial Fourier domain known as K-Space. Fourier analysis of the PSF is a challenge as the PSF is nonlinea and spatially variant. It is common for the PSF to be approximated to overcome these lim-itations. Here the PSF is approximated as a convolutional back projection kernel (CBPK). The limitation of this approximation is that it only holds for small scene sizes, while its strength is that it holds for all possible BSAR collection geometries. Coherent imaging is approximated as a convolution operation between the stationary scene's reectivity function and the CBPK. In this dissertation, Fourier analysis is used to describe the BSAR imaging process. The multi-dimensional Fourier description is coupled with concepts from dierential geometry to classify BSAR imaging into a variety of clearly identiable classes. This clas-sication process is employed to show how any generic BSAR imaging process with its own PSF may be mapped to a more desirable PSF by implementing frequency and amplitude modulation. This capability makes the analysis approach developed in this dissertation a versatile design tool for all forms of coherent bistatic imaging scenarios.