synthetic aperture radar (sar) uses relative motion to produce fine resolution images from microwave frequencies and is a useful tool for regular monitoring and mapping applications. unfortunately, if target distance is estimated poorly, then phase errors are incurred in the data, producing a blurry reconstruction of the image. in this talk, we introduce a multistatic methodology for determining these phase errors from interferometry-inspired combinations of signals. to motivate this, we first consider a more general problem called phase retrieval, in which a signal is reconstructed from linear measurements whose phases are either unreliable or unavailable. we apply certain ideas from phase retrieval to resolve phase errors in sar; specifically, we use bistatic techniques to measure relative phases and then apply a graph-theoretic phase retrieval algorithm to recover the phase errors. we conclude by devising an image reconstruction procedure based on this algorithm, and we provide simulations that demonstrate stability to noise.